They conducted a survey focused on three questionnaires: the Critical Gender Affirmation Scale (CGAS), which they created, Left-Wing Authoritarianism (LWA), and justification of political violence. They tried to correlate these measures to each other, and claimed that their findings “suggest that gender ideology is associated with LWA and willingness to justify political violence”.

This certainly sounds plausible and satisfies those of us critical of gender ideology, but the study used invalid measures and statistics and withheld critical information, such that it’s not possible to interpret their findings or know if they’re true. They also failed to release their data, something that shouldn’t be tolerated in any scientific setting. (We should probably organize to enforce a rule that if you don’t release your data, we’re not acknowledging your “study” and will not talk about or cite it, and media should not report it.)

Correlating Survey Scales

Social science researchers often correlate scores on one survey scale with another, and these correlations are often invalid and false.

Take a look at this survey item:

God’s laws about abortion, pornography and marriage must be strictly followed before it is too late, and those who break them must be strongly punished.

You’re given a 9-point response scale of disagreement to agreement.

Let’s say the study also asks for your political orientation on a crude left-right scale, you identify yourself as center-right, and you answer 5-Neutral to the question above. Or even 4-Slightly Disagree. Say you oppose abortion, you’re ambivalent about porn, and you think marriage is a critical institution, but you don’t know what the item is talking about regarding “God’s laws” on each of these things, or how/why we’d “strongly punish” people who break said laws. (Jail people who divorce or cheat?)

The item is from the Right-Wing Authoritarianism (RWA) scale (Altemeyer), and you can infer from it and the other RWA items that the researchers are hostile to conservatives, have cartoonish views of them/you, and are trying to set you up.

Now imagine that most conservatives answer 4-5 on the 9-point scale. That is, conservatives tend not to endorse the item. And say leftists are at the floor — they’re out as soon as they see “God’s laws”, clustering at 1-Very Strongly Disagree.

In fact, that’s what the data often looks like. Yet conservatism reliably correlates with “Right-Wing Authoritarianism”, even though conservatives tend not to endorse the ridiculous items. How is this possible? Because correlation is this:

Let’s say X is political orientation, where 1 is far left and 7 is far right. Y is Right-Wing Authoritarianism. All we need for a correlation is for people with above-average scores on political orientation (conservatives) to also tend to have above-average scores on RWA. It doesn’t matter if all the variance is at the bottom of the scale. If leftists cluster at 1-2 and conservatives at 4-5 on the 1-9 RWA scale, then conservatives will be above the mean most of the time and leftists below it. No one needs to actually endorse the views/items on the scale to get the correlation to work out. You can do the math in your head.

Here’s a visualization of what’s happening.

For decades, leftist social psychologists have made claims like “Conservatism predicts Right-Wing Authoritarianism” when most conservatives in their samples did not endorse the RWA items. This makes their claims false — a statistical correlation doesn’t matter if the participants didn’t actually express the requisite views. By the laws of the English language, conservatism doesn’t predict RWA in such cases, because only the agreement side of the Right-Wing Authoritarianism items could represent Right-Wing Authoritarianism (were the items valid and single-barreled, which they’re not). Here’s an example of the phenomenon from Conway et al. (2018):

These measures used a 7-point scale. As you can see, conservatives averaged well below the neutral midpoint of 4 on RWA and liberals averaged just below the midpoint of the Left-Wing Authoritarianism scale. Note that this was a different, even worse LWA scale than what the HHS researchers used, including absurd items like “What our country really needs is a strong, determined leader who will crush the evil of pushy Christian religious people, and take us forward to our true path.” (Amazing that leftists scored as high as they did.) Conway and colleagues tried to mirror the ancient, ridiculous RWA items, which is pointless because RWA was always an invalid caricature scale with cartoonish, multi-barreled items — it cannot be used to measure right-wing authoritarianism.

This phenomenon of leftist academics invalidly using correlation analysis to link conservatives to damaging views they don’t actually hold is widespread, spanning conspiracy beliefs and invalid scales like “Social Dominance Orientation” (SDO), “Modern Racism”, etc. (See Addendum B below for more on this.)

Left-Wing Authoritarianism

The HHS researchers used the new 13-item Left-Wing Authoritarianism scale created by Costello & Patrick (2022). However, they didn’t disclose the results. We don’t know if leftists generally endorsed the items, potentially mirroring the same issue we see above in how left-wing academics report conservativism as “predicting” Right-Wing Authoritarianism when they don’t actually endorse the items.

The deeper issue is that the LWA scale is invalid and cannot be used to measure left-wing authoritarianism.

Example items:

All political conservatives are fools.

People who are truly worried about terrorism should shift their focus to the nutjobs on the far-right.

Anyone who opposes gay marriage must be homophobic.

What do these items have to do with authoritarianism? I have no idea why they’re in the scale.

And:

America would be much better off if all of the rich people were at the bottom of the social ladder.

What? Bottom of the social ladder? What does that mean? That’s not how Americans talk. And what does it have to do with authoritarianism? It sounds like envy and resentment.

Since the scale is invalid, with several items that don’t measure authoritarianism, any correlation with gender affirmation is moot. Even if gender-affirming participants generally did endorse the LWA items (which we don’t yet know because the researchers withheld the results), we can’t call the scale any kind of “authoritarianism”.

The LWA scale missed a huge opportunity to capture leftist authoritarianism as we observe it in the wild. Even though it was released two years after COVID, which spawned a massive display of leftist authoritarianism, it has no questions about forced COVID vaccination, forced masking, or lockdowns. Or any forced vaccination questions, like making all girls get the HPV shots.

There’s nothing about censoring “misinformation”, support for lawfare against Trump, criminalizing “misgendering”, banning homeschooling, mandatory indoctrination in leftist DEI, race, and gender ideologies in schools and workplaces, canceling or getting people fired for non-leftist speech, or taking children away from parents who won’t “affirm” their “gender identity” (i.e. any non-leftist or dissident leftist parents).

Instead, the LWA scale tries to mirror the ancient RWA scale, leaving us with two invalid caricature scales loaded with cartoonish items, many of which have nothing to do with authoritarianism.

Given that the HHS researchers did not report the LWA scores, what did they report? Only inferential statistics, e.g. “CGAS significantly predicted LWA, β = 0.56, R² = 0.315, F(1, 1206) = 554.60, p < 0.001”.

None of that razzle-dazzle means anything if people who endorsed the gender items didn’t endorse the LWA items — that is, if the variance in LWA was at the bottom of the scale, as we see in the RWA animation above. The tiniest p-values mean nothing if people didn’t endorse the items. In this case, it’s worse because the LWA scale doesn’t validly measure authoritarianism anyway, so all the inferential statistics in the world are irrelevant1. This is a common weakness of academic social scientists — they love to traffic in inferential statistics, skipping right over the data and basic descriptive stats, and missing key validity problems. It would be easy for non-leftist researchers to set a higher standard of rigor and validity than the academic norm, and it’s disappointing that the HHS team did not do so.

Choosing Violence

When it comes to support for violence, the HHS researchers didn’t disclose the questions participants answered, all of the response options, or the results. This means, again, that we can’t do anything with the reported correlations.

They say that the questions had to do with killing Charlie Kirk or Donald Trump, “damaging the property of wealthy people”, etc., but they don’t disclose the question wording, which is critical. We don’t know what participants were asked.

They also don’t disclose most of the response options. They used a 5-point scale, and only disclose that 1 was “Not at all justified” and 5 was “Completely justified” — we don’t know how the other options were labeled. If, as I suspect, most of the variance was between the 1 and 2 options, the 2 might not represent meaningful support for violence depending on how it’s worded.

Moreover, using a 1-5 scale where “1” is the only non-endorsing option and 2-5 are all levels of endorsement is rigged to pick up endorsement of violence. Krosnick's work on satisficing in survey response applies: respondents who are only mildly engaged, or who are ERS-avoidant, will drift off the true floor for reasons that have nothing to do with their actual attitude, and with no symmetric "mirror" option on the other end, that drift reads as substantive endorsement rather than noise. It’s bad psychometric practice and should never happen in 21st-century research.

And as with LWA, the researchers didn’t disclose the results. They provide no descriptive statistics for the violence scale — no mean, SD, median, nothing. In fact, the entire report is missing descriptive statistics. Instead, it’s full of useless inferential statistics. A quick primer:

Descriptive statistics are means/averages, medians, percentages, where participants clustered. It’s what Gallup would report.

Inferential statistics are attempts to relate variables to each other and come with p-values attached — tests of the likelihood of the observed relation assuming the null hypothesis was true. Examples include correlation, regression, t-tests, structural equation models, etc. Inferential statistics can be very abstract, are often misleading, and most people have no way to understand or assess them. Gallup, Pew, et al. generally don’t use them.

Doing it right

Here are some quick notes on how this research can be done validly.

Authoritarianism

These items are a good starting point for a valid Left-Wing Authoritarianism scale that I predict will see more endorsement from leftists, as they better reflect the positions we see in today’s landscape. (Ideally, several leftists would be brought into the process of LWA scale development, just as plenty of conservatives should be included in the development of an RWA scale.) These items are cleaner and single-barreled. Most importantly, they actually tap into authoritarianism:

1. COVID vaccination should be mandatory for all adults.

2. COVID vaccination should be mandatory for adults and children.

3. Measles vaccination should be mandatory for all children.

4. HPV vaccination should be mandatory for all girls.

5. The government should regulate misinformation.

6. Freedom of speech does not give people the right to post misinformation.

7. It was appropriate to mandate masking indoors during the COVID pandemic.

8. It was appropriate to mandate masking outdoors during the COVID pandemic.

9. It’s fine for employers to fire people who refuse the COVID vaccines.

10. The state should remove children from parents who don’t affirm their gender identity.

11. The government should {shut down/fine} FOX News for spreading misinformation.

12. People should be required by law to use a person’s correct pronouns.

13. Misgendering should be a crime.

14. Homeschooling should be banned.

15. Homeschooling should be tightly regulated, with monthly inspections by regulators.

16. Only licensed librarians should decide what books are appropriate for children.

17. Residential school denialism should be punishable by law. (Canada-only item.)

18. Media outlets who reported critical stories on Jason Arday should be punished. (British-only item, needs refinement.)

This is only a starting point. We’d need to think through the appropriate lexical, semantic, and grammatical structure, which the LWA creators did not do. (Social psychologists don’t generally cover this ground.) And we’d need to test these items and different versions of them. But already, they’re cleaner and easier to understand than the LWA items, and they reflect modern American leftist discourse. A valid LWA scale might need continuous updating to keep up with the evolving political landscape (social psychologists’ habit of using decades-old political scales is extremely awkward). COVID was a stunning display of left-wing authoritarianism and any post-COVID scales should tap into it.

Violence

A challenge with assessing leftist support for violence and authoritarianism is that leftists have lots of false beliefs and those false beliefs might drive their support for violence and authoritarianism. I suspect that there’s a psychological disposition at work as well, but it’s an empirical question.

For example, if they think torching ICE buildings or killing Trump is justified, they might think ICE has murdered people, having seen only one-sided coverage of ICE shootings. They might think Trump is a murderer, through ICE, and lately they mysteriously adopted the belief that he’s a “pedophile”, in addition to longstanding false beliefs that he’s a rapist, that he called for violence on J6, that his supporters killed police, that he committed “34 felonies” (derived from a capricious NY criminal case centered on nonfelonious hush money payments), etc.

As for Charlie Kirk, many leftists think he advocated violence or even murder. After he was murdered (by a leftist), lots of influencers and columnists lied about his views. For example, the Washington Post’s Karen Attiah wrote:

“Refusing to tear my clothes and smear ashes on my face in performative mourning for a white man that espoused violence is…. not the same as violence.”

(Charlie did not espouse violence.)

Many people circulated this quote from Charlie, often framed as being about mass school shootings specifically:

I think it’s worth it to have a cost of, unfortunately, some gun deaths every single year so that we can have the Second Amendment to protect our other God-given rights.

He was not talking about school shootings specifically, and his full statement talked about how to reduce such horrors.

Asking leftists about violence against people they think are murderers or murder-enablers might not give us a clean picture (and we’d want to ask conservatives about their support for violence against Dems they think are murderers and whatnot). To rigorously asses their support for violence:

1. Don’t focus on murder. Present all sorts of lesser forms of violence to get more variance and clarity. (Since we won’t be pulling the 1-vs-4 point scale trick used in the HHS report.)

2. Use targets who leftists don’t think are murderers, murder-enablers2, rapists, pedophiles, etc.

A common dynamic with leftists is denial. That is, they deny that something awful is happening, like the surgical mutilation and chemical sterilization of thousands of children on leftist “trans” grounds, schools secretly transitioning children without their parents’ knowledge, antifa violence nationwide, broad leftist violence, censorship of non-leftist discourse on social media, in some cases prompted by government officials, etc.

This gives me an idea for a different approach to capturing leftist support for violence: Present them with facts about leftist violence and see if they downplay it. This will require careful study design, but there are lots of possibilities. The theory here is that downplaying violence is a form of support for that violence.

More measurement challenges

Leftist misinformation also complicates a measure of left-wing authoritarianism. For example, leftists held wildly false beliefs about the COVID vaccines decisively preventing infection and transmission (claims infamously spread by Biden and Maddow, among others), and this belief might have driven their support for forced vaccination.

Note that I’m not saying that if these beliefs about the vaccines were true, they logically imply support for forced vaccination. Various available accounts of individual rights can make any COVID vaccine characteristics irrelevant to the question, and there are many other considerations and arguments that could drive opposition to forced injection of these products. I’m just saying that for leftists, these (false) beliefs about the vaccines might’ve driven their authoritarian/totalitarian stances.

Legacy/leftist media also overstated COVID mortality rates and presented COVID as a risk to everyone when in fact COVID isn’t relevant to most people in terms of mortality or hospitalization risk, making the vaccines irrelevant for those risks. Thus, leftist support for forced vaccination could be driven by a false belief that they have tangible lifesaving benefits for everyone.

And authoritarianism scale items about censoring or regulating “misinformation” expose some tricky issues. As we saw during COVID, any leftist/Democratic censorship of purported misinformation will likely label facts, views on unresolved, brand new scientific questions, and even policy opinions as “misinformation”. Ultimately, all conservative, libertarian, or counter-leftist views and narratives are at risk of being so labeled. For example, leftist media like CNN’s Brian Stelter (of the Reliable Sources show) who position themselves as anti-misinformation police very often make false claims and spread misinformation, which they don’t correct:

In March 2020, Trump noted that the death rate was likely less than 1%, which was correct. Stelter was wrong, and he didn’t even try to present any evidence – that bit that Trump’s claim was “so at odds with what health experts are saying” was all he said3.

If we want to unpack leftist support for censoring “misinformation”, we’ll have to grapple with the possibility that they don’t know that in practice this means censoring the truth and undercutting the normal scientific processes of dissent, discovery, and debate. It could be that leftist authoritarianism and justification of violence is entirely driven by false beliefs (“misinformation”), with no distinct psychological propensity. Or not. It’s an empirical question, and the nature of popular false leftist beliefs might change their psychology in ways that make it difficult to distinguish these factors.

Alarmingly, some leftists define misinformation not as false claims but as deviation from some authority’s statements, usually a government agency. That’s a noteworthy epistemic collapse and it exposes a deeply authoritarian epistemology that should be of great interest to researchers exploring left-wing authoritarianism. Possible items:

Health claims that deviate from the CDC’s guidance are misinformation.

Scientific claims that deviate from statements by the appropriate government agency are misinformation.

Conclusion

I think there is likely an association between genderism and support for violence and leftist authoritarianism. It’s just that the HHS study doesn’t give us any evidence of such associations. They didn’t actually measure authoritarianism, and they withheld the scores for both authoritarianism and justification of violence, along with the violence questions and response options.

This is disappointing because this is what academic researchers normally do, and the HHS team had an opportunity to rise above those low standards and set a new one. They didn’t. Also, anything of this nature coming from the Trump administration will be greeted with suspicion and extra scrutiny. That the researchers didn’t anticipate this and deliver a much higher quality product is, again, disappointing.

Joe Duarte grew up in small copper mining towns in southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find his most important journal article here. Find him on X here and reach him at gravity at protonmail.com.

Addendum A: Where’s Joe?

I’m critical of a lot of published research and I’m operating at a higher level of methodological validity and rigor than most academic social psychologists, but I haven’t published any empirical research myself. (All my peer-reviewed journal publications are methodological, focused on political bias in psychology research, or invalid climate consensus research. Plus a handbook chapter on improving positive psychology research — more methodology.)

This feels increasingly awkward. Why haven’t I published any empirical work?

In graduate school, I decided not to.

Specifically, I decided that I wasn’t going to publish anything based on student samples — the default junk sampling method of social psychology. I didn’t want to make any claims about human psychology — human nature — based only on college kids. And not just college kids, but the college kids at the university at which I happened to find myself, which is arbitrary and overly specific, further weakening validity.

I bolstered my dissertation package of studies with adult samples from Amazon’s MTurk online worker pool, but I wasn’t happy with that either. I wasn’t interested in publishing studies of antisemitism based on such samples and methods, and had designs on much bigger and more rigorous studies.

That was a conscious, considered decision, but there was another factor: I was diagnosed with thyroid cancer in grad school, my thyroid was removed, I underwent daily external beam radiation therapy for a month (very unusual in thyroid cancer cases, might’ve been a mistake, long story), and I fell into a long, long period of debilitating chronic fatigue. Lots of people are fine just taking Synthroid for the rest of their lives after losing their thyroids to cancer, but for whatever reason I was in the ≈10% who are not fine.

So I wasn’t going to tear it up, blazing trails with innovative, more valid research methods. I could barely get out of my apartment much of the time.

But I also knew I didn’t want my name on anything false or invalid in a journal article, so I wasn’t going to spit out the ridiculous junk studies so common in social psychology.

By the end of grad school, I also hated the journals… :-) I despise them for publishing so much fraud and false claims. There’s no way I would submit anything to Holden Thorp’s Science, or to Nature, Psych Science, or any of these ridiculous journals. They’re explicitly and maniacally politically biased, they publish “science” embedded with leftist ideology, they frequently publish false, invalid, and/or fraudulent papers, and they even require Orwellian leftist language (e.g. I would never refer to a man as a “trans woman”). They’re a disgrace and I don’t want my name in them.

But I’ve now decided to publish empirical work. I need to do more than expose invalid junk and actually demonstrate what serious research looks like. And there are interesting questions to answer, for example the nature and extent of left-wing authoritarianism. Where to publish my work is a separate problem, but I think there might be some decent journal options now. We’ll see. In any case, Joey D is getting back in the game.

Addendum B

There is no rule or standard in academic psychology research that says that we cannot say “Membership in Group X predicts belief Y” when most of Group X does not believe in Y.

I don’t much care that no one in the field makes this point. I’m making it, because it’s ethically, logically, and semantically obvious that we can’t do that, because such statements are false.

Nor is there a rule or standard in academic psychology research that says that we cannot say “Membership in Group X predicts belief Y” when most of Group X explicitly disagrees with Y. For example, conservatives saying they slightly or moderately disagree with the RWA items, or this ditty from the Social Dominance Orientation (SDO) scale: “Some groups of people must be kept in their place.”

The fact that social psychologists (and similar fields) allow themselves to make such false claims just means that the field is not credible or rigorous. Ultimately, I’m confident that as these and similar issues surface, taxpayers, legislators, and real scientists in real scientific fields will agree with me that when social psychologists claim that “Membership in Group X predicts belief Y” when most of Group X does not believe in Y, they’re making false claims and this should not be tolerated or funded.

And such false claims are aggravated when resting on invalid caricature scales loaded with cartoonish, multi-barreled items written by radical leftists to defame conservatives. This is not science of any description.

Cambridge sociologist of education Jason Arday is in the news as a serial liar and plagiarist. He fabricated claims that he was sent a pig’s head, that he ran 600 miles in six days, and that a masked, knife-wielding man threatened him on Cambridge grounds. He called the police on a journalist and successfully intimidated Times Higher Education into killing an exposé on him. At this point, it’s wise to assume that his entire life story is a fiction, including his claimed autism diagnosis and miracle cure, that he was illiterate until age 18, and any international charity adventures.

As philosopher Nathan Cofnas has detailed, Arday also plagiarized his dissertation, and quite extensively. Even if he had actually written it, no one should be awarded a doctorate in exchange for such awful work — a barren, badly written catalog of inertly received ideology littered with typos, starting in the title itself.

That he was awarded a doctorate, and that several universities actually hired him as faculty, culminating in Cambridge of all places, marks several additional scandals worthy of inquiry.

But Arday also plagiarized his data, such as it is, in a published study, which I’ve never heard of. How does one plagiarize data? Well, plagiarism is the theft of someone else’s words, so if your data consists of words…

Background

Arday publishes junk studies where he claims to have interviewed a few minority students, then purportedly quotes from those interviews. There’s no further analysis, not even systematic text analysis, just some painfully written discussion of the alleged quotes. Apparently, sociologists don’t provide the full quotes when they use this method (e.g. in an Appendix), making the studies pointless since it’s too easy for biased researchers to selectively quote people and exclude some participants entirely.

He frames his “studies” around the new leftist race ideology — Critical Race Theory (CRT) — which sorts humans into racial groups, defines them by their race and other group “identities”, and asserts that minorities are surrounded and grievously harmed by “systemic”, “structural”, or “institutional” racism, that we’re “marginalized” and “oppressed”, and that “systems” must be “decolonized”, including academia, in order to end this alleged state of marginalization and oppression.

To the extent these claims can be falsified, they’re false. There’s no systemic racism against blacks and browns in 2026, nor has there been for many decades, and the reality is in fact the opposite — it’s an advantage to be a minority in many contexts, both in the UK and the US. For example, it’s easier to gain admittance at every academic stage (elite high schools, college, graduate programs, medical schools, law schools, etc.) and easier to get a faculty job. That is, minorities can secure admission with lower test scores and grades, and secure job offers with fewer and lower quality publications, than white applicants1. It’s not minorities who suffer discrimination — it’s white males and especially non-leftists of all colors.

Reality has an anti-leftist bias and since sociology is dominated by radical leftists the field is not a reality-congruent domain. Moreover, ideological constructs like “marginalized” and “oppressed” applied to the 21st-century West mark a novel psychology and optional cosmology rather than testable, coherent claims about the world. You won’t find constructs of that nature in other ideologies and schools (conservative, libertarian, etc.)

Further, it’s invalid for scholars to assert an ideology as descriptive reality. That’s the core problem with leftist dominance of academia — they don’t understand the difference between descriptive reality and their ideological tenets. That is, they don’t understand the difference between saying “service users can be acutely aware of the provider’s framed credentials” and “service users can be acutely aware of the centrality of whiteness” (Arday) — one refers to descriptive referents (a framed degree on a wall) while the other embeds a hostile abstract construct drawn from a particular, non-universal political ideology, a construct that doesn’t refer to something consensually real and which is not scientifically valid or meaningful. (My colleagues and I touch on this in our Behavioral and Brain Sciences article on ideological bias in psychology research.)

Leftist race ideology also packages a new and extraordinary psychology of chronic grievance and external locus of control — a recipe for bitterness, misery, and underachievement, which will ironically prevent minorities from closing income and educational gaps. In this framework, which saturates Arday’s article, minorities are not responsible for their behavior, suffering, or life circumstances. Every vicissitude, every discomfort, is an incident of “racialized” victimization. Everyone they interact with must cater to the minority’s imagined needs, but the minority will never be satisfied. If there are difficulties with a therapist, it’s the therapist who must change. Even when therapists can’t understand a client’s poor English, the therapist is in the wrong (and is probably lying). In this framework, there’s no curiosity, benevolence, or interest in objectivity or truth-seeking with regard to any frustrating experience — if a minority thinks they’ve been wronged (as we’re stridently encouraged to do), then they’ve been wronged. And someone has to pay. This acidic, narcissistic psycho-emotional philosophy alone deserves immediate attention and full characterization by serious researchers.

Plagiarized Data

Retired academic Dave Harris discovered the first instance of data plagiarism.

Data Plagiarism #1: Passive voice pap

The need for a positive interpersonal connection between service user and healthcare provider where the consultation was “a dialogue” (Male D. FG1) was seen as an important factor in engaging with mental health services.

It’s from Memon et al. (2016). They interviewed 26 minorities in Southeast England about access to mental health services.

In 2018, Arday published a study titled Understanding Mental Health: What Are the Issues for Black and Ethnic Minority Students at University?, where he claims to have interviewed 32 minority students about mental health issues at university. Harris found this passage:

‘Positive, interpersonal dialogue between service users and healthcare providers’ (Female, Black, UG, 27) where the consultation was ‘a reciprocal dialogue’ (Male, Asian, PG, 25). This was considered to be an important factor in facilitating better ethnic minority engagement with mental health services at universities and wider society.

Note that the original Memon et al. passage is largely unquoted — it’s the writing of the authors, not the quoted words of a participant. Arday converted Memon’s body text into two participants’ quotes, copying most of the passage and converting “was seen as” to “was considered to be”, along with some other minor tweaks.

He fabricated the participants’ quotes, meaning there may not have been any participants — or any study — or perhaps they didn’t say what he wanted them to say.

I found many more examples where Arday stole participant quotes from Memon et al. These examples are all drawn from his originally published paper (there have been several “corrections” since).

Data Plagiarism #2: Pen strokes and the dose makes the poison

Here’s Memon:

There was concern that clinicians had “the power simply with the stroke of a pen to decide whether you are going to be sectioned or whether you are going to be sent away for some counselling. Or […] administered some really, really high doses of whatever kind of medicine” (Male D. FG1).

Here’s Arday:

There was concern that, in some cases, clinicians could potentially abuse their power. ‘Let’s face it . . . with the stroke of a pen you could be sectioned or sent away for some counseling. Or … have administered some ridiculous dose of whatever kind of medicine’ (Male, Black, UG, 13).

This is near verbatim theft, and he even copied the ellipses after Or...

Data Plagiarism #3: West Indian particulars

Memon:

“In the West Indian community […] people do not trust psychiatrists particularly […] in terms of where my family and friends would turn...” (Male D. FG1).

Arday:

‘Within African and West Indian communities . . . people do not trust psychiatrists in particular.’ (Female, Black, UG, 13).

Arday merely changed particularly to in particular, and he again copied the ellipses…

Data Plagiarism #4: Really, really

Memon:

“the mental health service does not always understand. That we are really, really isolated” (Female I. FG1). Social isolation was also relevant for people who lived alone, or whose family structure had broken down…

Arday:

‘The mental health services provided at universities did not always understand all of the racialized nuances involved’ (Male, Mixed-Heritage, PG, 1). Further, one participant added that we are really, really isolated in this space as black people’ (Female, Latin-American, UG, 7).

Social isolation external from university environments was also relevant for participants who lived alone or whose family structure was fractured for varying reasons.

Arday copied the “mental health services does not always understand” and the “that we are really, really isolated” bits (he’s missing the opening quote there). He even copied Memon’s sentence after the quote, breaking it off into a new paragraph and making minor changes.

And again, Arday industriously converts one of Memon’s participants into two fictional participants of his own.

Strangely, Arday also copied the section headings from Memon (see my Appendix below). His paper is a mirror of Memon et al.

At this point, I’m bored with the examples. There’s much more in Arday’s paper for any serious examiner to find, but I’m not going to write it all up or do Cambridge’s work for them for free. Arday is an extraordinary fraud even by academic standards and there’s no excuse for Cambridge to have even thought about hiring him or allowing him on campus. If Hilary Cremin, the Head of the Faculty of Education who was so excited about hiring him, actually read his work and thought it was good, she may have cognitive disabilities of her own.

It’s possible that the cult psychology of the left and the way that leftists treat minorities as pets instead of adult humans can undermine a smart person’s judgment such that they genuinely see morons and frauds like Arday as smart, but I’d argue that this still counts as an actionable cognitive disability. At the very least, it means that such people cannot perform the job of a scholar and should be promptly dismissed from their academic posts. We’re trying to have a civilization here.

The fraudulent study was published in the journal Social Sciences. Instead of retracting the article and reporting Arday to Cambridge as they were ethically obligated to do, the journal allowed him to make “corrections”, making themselves party to his fraud. He deleted the plagiarized quotes I cited above. Strangely, he introduced more errors. The original opening sentence of “Increasing diagnoses of mental illness…” became “Increasing diagoneses…” in the corrected version, joining several other new errors.

The journal’s Correction Notice says “Previously, the statement that the research had been adapted from Memon et al. (2016) appeared in Section 4, but inadvertently omitted overlap between the two publications in prior sections.”

This is extraordinary. The man plagiarized Memon et al. and they’re trying to cast this fraud as “adapted from”? This is nonsense. They’re party to the fraud now. And “overlap” between the two publications? Again, this is extraordinary. The Social Sciences editors are trying to permit plagiarism in broad daylight by renaming it “overlap”. They should be fired from all academic positions and journal roles — we cannot tolerate frauds.

In any case, we should assume the new quotes in the corrected version are Arday’s bespoke fabrications, since his original paper consisted of fake quotes plagiarized from Memon et al. There’s no reason for any of his papers to remain published by any journal — they should all be retracted.

Post-Truth

Thousands of liars signed a leftist activist group’s petition claiming that the plagiarism accusations against Arday are false.

The plagiarism accusations are not false. Arday is a plagiarist. Beyond the plagiarized data I found, here’s an example from his dissertation:

They really said “Entirely false allegations of plagiarism were made against Professor Arday and referred to two universities. Investigations have found no evidence whatsoever of wrongdoing. The relevant academic journals also found no basis for the claims.”

This is where you have to weigh the evidence of your senses against “two universities”, “investigations”, and “relevant academic journals”.

Get used to it — this is the Orwellian world the left has created. This is what they do, what they’ve done to our institutions. It’s quite stressful for most people to have to stand against authorities and mad crowds who deny reality at a sensory level. It’s certainly confusing to see people lie about basic, observable facts. It’s like Asch’s line-length conformity experiments. Feel free to stand on the shoulders of Orwell, Jefferson, Rand, and Rowling, Queen of the Angli, Breaker of Chains.

Over and over again, leftists appeal to authorities while evading the substantive facts of the matter. They did it with Anthony Fauci, with pediatric sex-rejecting procedures that sterilize children, with the lack of good evidence for COVID lockdowns or mask efficacy. Leftism is an authoritarian-totalitarian creed and even includes an authoritarian epistemology. This is the clearest plagiarism case you’re likely to encounter in the course of your life, but people like Simon Baron-Cohen, the Good Law Project, and Cambridge officials are willing to lie to your face about it. They’re willing to lie to you about anything and everything if their abhorrent political ideology is activated.

Academia is a fraud-heavy place, especially fraud that advances leftist ideology — since leftism is comprehensively false, it advances through lies, misinformation, mass indoctrination of children, lurid hoaxes (Smollett, starving Gaza children, Michael Brown, Arday), censorship, and Orwellian word games. Cambridge has made it clear that they adamantly do not care about truth or scientific integrity. Their leaders are willing to brazenly lie to the public. This suggests that fraud abounds at Cambridge. Interested parties would do well to look at politicized departments like sociology, psychology, education, climate science, anthropology, anything involving “gender”, etc. You’ll find much more fraud — I guarantee it.

Joe Duarte is a first-generation American of Mexican descent, grew up in small copper mining towns in southern Arizona. He earned his PhD in social psychology, focused on the role of envy in antisemitism, and was a National Science Foundation Graduate Research Fellow. He focuses on political bias in media and academic research. You can find him on X here and reach him at gravity at protonmail.com.

Appendix: The copied section headings

The Memon et al. Results section is broken into these subsections:

Personal and environmental factors

- Recognition of mental health problems

- Social networks

- Sex differences

- Cultural identity and stigma

- Financial factors

Relationship between service user and healthcare provider

- Waiting times

- Language

- Communication

- Responding to needs

- Power and authority

- Cultural naivety, insensitivity and discrimination

- Awareness of services

Arday’s copied most of these. Here are his headings:

Personal and Environmental Factors

- Recognition of Mental Health Problems and Fear of Stigmatization

- Social Networks

- Gender Differences

- Cultural Identity and Stigma

Relationship between Service User and Healthcare Provider Situated around Power and Hierarchy

- Language

- Communication: Cultural Naivety, Insensitivity, and Discrimination (combining two of Memon’s sections into one)

- Power and Authority

Consider the captivating journal article “An alternative carrier solvent for fingermark enhancement reagents”, published in Forensic Science International.

It was counted as part of the purported 99% scientific consensus on human-caused climate change by Lynas, et al. (2021).

It’s a paper about solvents for exposing fingerprints. It has nothing to do with climate science or the focused empirical question of human contributions to the observed warming. Why was it included?

It was included because it mentioned global warming in its abstract. I kid you notly. This obviously hints at a deeper problem. Why would a journal article be counted as part of a “consensus” to begin with (since the unit of consensus is people, not articles), why is the abstract relevant, who cares if an abstract mentions global warming, and how could a paper about fingerprint solvents possibly be included? (You can read the abstract in Appendix A below.)

It’s not an outlier. Here are some other articles that were counted as relevant “climate science” papers and included in the false 99% estimate:

• “Climate justice, small island developing states & cultural loss”. This is a political philosophy paper that applies the leftist construct of “climate justice”, written by a random political theory lecturer in Ireland.

• “Calcium leaching from waste steelmaking slag: Significance of leachate chemistry and effects on slag grain mineralogy”. This is a paper about calcium leaching from steelmaking slag.

• “Properties and corrosion behaviors of mild steel in biodiesel-diesel blends”. This is a paper about steel.

• “Comparison of refrigerated warehouse energy demand with R-717 and R-507 using eQUEST model”. This is a paper about a refrigerant comparison test in a Cincinnati warehouse.

None of these papers have anything to do with climate science. Some of them have nothing to do with science. What happened here?

This climate change consensus study was conducted by leftist activists who did the following:

1. Not knowing how to perform a literature search or a systematic review on pertinent climate science questions, they simply searched the academic literature for the keywords “climate change”, “global climate change”, and “global warming”.

2. They took a random sample of 3,000 of the resulting papers and had their team of lay activists read the abstracts — just the abstracts.

3. Their team of activists rated the abstracts on whether they took a position on human-caused warming and their level of endorsement of the proposition that humans are causing warming.

4. They then counted the papers/abstracts they scored as endorsing the proposition and divided that by the total that they claimed took a position on the topic. They called the resulting percentage the “scientific consensus” on human-caused climate change.

Why would anyone do this? It’s not a method known to science and has no name. It’s not a remotely valid way to measure a consensus on any topic. It’s a spurious recreational activity that has no epistemic standing — it tells us nothing about anything. As “research” published in a peer-reviewed journal article, it’s completely insane and marks an unprecedented collapse of scientific standards.

We don’t care about anyone’s subjective ratings of abstracts with respect to the authors’ views on a topic. That’s not anything. Given that Lynas and friends counted a fingerprint solvent paper and several steelmaking papers as relevant climate science research, they lack the intelligence to even implement their invalid method properly. But even if restricted to relevant climate change attribution papers, their method would still be invalid — it would still be spurious recreational activity that has no place in a peer-reviewed journal.

We simply can’t do anything with a count of papers based on someone’s opinions of their abstracts, the set based on a keyword search, or any subsequent acts of division or other arithmetic. That’s not anything. Yet this study was widely promoted by legacy leftist media like the Guardian:

These activists didn’t come up with this on their own. They copied the method from an earlier team of activists who published the infamous 97% consensus paper (Cook, et al., 2012), which was also fraudulent in addition to being invalid. It was Cook who had the deranged idea of having lay leftist activists working from home (including the founder of Timbuk2 luggage) rate the abstracts of thousands of irrelevant journal articles they didn’t understand, drawn from “climate change” keyword matching. Cook’s 97% “climate science consensus” includes a welding paper by Volvo engineers, several other steelmaking papers, several diarrhea papers, an article on cooking stove use, etc. (More here.) But again, the deeper issue is that the core method of rating and counting abstracts is irreparably invalid.

The same journal — Environmental Research Letters — published both of these papers, under Editor-in-Chief Daniel Kammen. He’s a deranged political partisan — in 2017 he resigned as a State Department Science Envoy and in his resignation letter to President Trump, he spelled out the word IMPEACH with the first letter of each paragraph… (seven months into Trump’s first term). That’s the kind of people we have waving the banner of “science”. See more on Kammen in Appendix B.

To measure the consensus on human-caused warming, we’d survey relevant climate scientists with smart, focused, well-written questions on human causation, the likely extent of any human contribution, confidence in various future projections and scenarios, confidence in any effects on non-warming variables like extreme weather, hurricanes, etc. The unit of consensus is people, not papers — papers cannot be used for several obvious reasons, and they certainly can’t be assessed by lay leftist activists (and not merely through their abstracts!).

That’s how you’d measure the climate science consensus. There’s no other core method — you have to ask the relevant scientists directly. Such surveys are best implemented by non-leftists, people committed to rigor. Note that we’d still need a reason to care about this topic and any consensus around it…

Zoom out

Caring about a “consensus” on any topic is optional and we need a fuller epistemological framework to have an informed stance on the question of consensus and in what contexts we’d take it into consideration. Referring to an expert consensus is an authoritarian heuristic and we only need heuristics in emergencies or when substantive, rational methods aren’t available. Recent history is full of false scientific consensi, especially when driven by leftist ideology, for example that abolishing the use of test scores in college admissions would be fine, that sterilizing and mutilating trans-identified children is supported by quality evidence1, various claims about racism in policing, claims that COVID vaccines prevented infection and transmission, and that there was good evidence that masking in public reduced COVID spread (it’s not clear why these random empirical COVID positions became leftist dogma, though they fit the authoritarian nature of the left).

Caring about climate change is also optional. By the somewhat abstract and computationally complex measure Global Mean Surface Temperature (GMST), the earth has warmed about 1.5 °C in 150 years, likely driven by human carbon emissions, at least in part. A moderate IPCC scenario might project another 1 °C of warming by 2100, an average of about 0.02 °F per year. You can care about that if you want, but there’s no way you or any other biped could notice it over the course of your life — even if you stayed in the same town for 70 years and it saw the same increase as GMST (moving would yield much larger changes up or down, destroying a “climate change” signal). The media can make you think you notice it, but that’s just the power of earth-on-fire imagery and propaganda. Being blasted with climate change propaganda is a profoundly novel way for humans to live, and it’s worth meditating on its effects. Warming is just (mild) change and we have no apparent reason to view the 19th-century baseline as optimal or to particularly care about a complex global average temperature (you live where you live), and cold is much deadlier to humans than warmth.

Enter Social Psychologists

Leftist activist and NYU social psychology professor Jay Van Bavel led a study that tried to trick participants into adopting a leftist ideological perspective on “climate change”. The researchers particularly wanted to spur activism for coercive left-wing policies like carbon taxes and even taxing meat and dairy (which would harm health). Research aimed at tricking participants to be more leftist is common in social psychology — leftists control the field and activism is an explicit goal of many researchers and organizations.

How did the study approach this? It fed participants propaganda like this:

“Did you know that 99% of expert climate scientists agree that the Earth is warming and climate change is happening, mainly because of human activity (for example, burning fossil fuels)? [Myers et al., Environmental Research Letters; Lynas et al., Environmental Research Letters; Doran and Zimmerman, EOS]”

Yes, they actually cited the insanely invalid Lynas study — the one that counted fingerprint solvent and steelmaking papers as quanta of climate science consensus.

Van Bavel and colleagues couldn’t be troubled to carefully read these journal articles. They just assumed they were valid and true, which is a huge mistake to make about academic output, but of course they’re academics and are unlikely to approach journal articles with the necessary exogenous intelligence and rigor, especially those that advance their political ideology.

They misled their participants by citing one false study (Lynas), one rigged study (Myers), and an old study that asked if human activity was a “significant” factor in warming, not whether warming was mainly due to human activity (Doran and Zimmerman, 2009).

And they didn’t cite any of the higher quality climate consensus research that gave lower estimates than these three cherry-picked papers. As I reported here, high quality studies by professional survey researchers had the consensus at 78-84%. Van Bavel and colleagues showed no rigor or real interest in the topic by not citing the much more valid Farnsworth and Lichter (2012), Bray and von Storch (2014), or Stenhouse, et al. (2014) and using their estimates instead of the 99%.

Key to these numbers was asking if human activity was responsible for most or all of the observed warming. In that case, the results were 78-84%. If asked simply if human activity was a factor or a significant factor, the results are in the high-90s, as with Doran and Zimmerman.

At this point it might not matter. In the decade since the 78-84% studies, the climate scientists who would say human activity is a factor but not the only or primary factor have probably been driven out of the field or retired, so it wouldn’t surprise me if a high-quality survey in 2026 resulted in a 95% or higher estimate.

Note that Van Bavel purports to study “misinformation” and yet he misled his participants citing an insane study that treated fingerprint solvent and philosophy papers as quanta of climate science consensus. Like every academic “misinformation researcher”, he spews a remarkable volume of misinformation into his host civilization. I’ve addressed some of his other false claims here and social psychologist Nathaniel Bork checked my work here.

Zoom In

The Lynas team are activists at an NGO called Alliance for Science…

Yes, sadly we’ve gotten used to leftists naming things the exact opposite of their true nature. Here’s what the “Alliance for Science” had to say about climate “misinformation”:

The second misinformation theme related to the claim that the ongoing climate emergency is ‘fake’ because warming so far has been “mild” and that “life on Earth thrived (and was far greener) when CO2 levels were at least 5X higher than today’s”.

They refer to “the ongoing climate emergency” as an inarguable descriptive fact. Whether 1.5 °C of warming in the last 150 years amounts to an emergency is a subjective judgment, an opinion on a matter of opinion. Leftist dogma is rife with this conversion of subjective appraisals on complex topics into inarguable descriptive facts, and the appraisals are often that something is an “emergency” or “crisis”.

They don’t just convert neurotic opinions on matters of opinion into facts. They treat this dissent as “misinformation” and then try to censor it, using government, social media companies, etc.

That this organization talks this way gives the lie to their mission being about “science”. This is ideology, not science. You can think climate change is an emergency or that it’s mild — that’s an opinion, an appraisal, and it isn’t a matter settled by science. The way different people even approach the question will vary, including the extent to which science is an input, and which science they refer to.

Lynas also said “the debate about whether climate change is real is well and truly over and is now almost entirely relegated to the lunatic fringe”.

This is a familiar formulation on the left — that “climate change is real”. Van Bavel also traffics in that phrase and seems to think that saying “climate change is real” says something important and makes the case for the whole leftist totalitarian policy package, from taxing energy to taxing or banning food that leftists have long targeted, especially meat.

Climate change is real? Okay… Yes, I’m real, you’re real, komodo dragons are real, and climate change is real. Now what?

The climate has always changed and always will. So what? Let’s tighten it up: I assume they mean that human-caused climate change is real.

Sure, though that’s awkward wording and “climate change” is too broad and vague. Let’s fix it to: human activity has contributed to the recent warming of ≈ 1.5 °C since the 1800s.

And let’s finish the thought by saying we’ll see more warming through 2100, maybe another 1.0 °C, and this will be partly or mostly driven by human activity.

All good? Now what?

Why would we care about warming? Well, we might care if 1) it affected us or humanity generally in significant, harmful ways, and 2) we could prevent these future harms, or reverse any harms we think are currently playing out.

Point #1 alone unfolds into a complex, highly variable topic. I myself have no climate harms to refer to. That is, I don’t know of any harms to me or to anyone I know. Nor do I anticipate any. Leftists increasingly group all bad weather, storms, hurricanes, even wildfires as human-caused “climate change”, but that’s not scientifically rigorous and it’s straightforwardly false in many cases.

Even if I wanted to do that, I wouldn’t be able to detect any impact on my life, say of 0.2 more extreme weather events per year where I live. I don’t know how to notice something like that, and the leftist lifestyle of consuming daily climate change propaganda that attributes so many random climate phenomena to an “ongoing climate emergency” is deeply irrational, unscientific, and unhealthy.

For Point #2, we’d need to be confident that there’s something we could do to prevent or reverse harmful climate change. I’m not sure why we’d believe that. Note that this proposition is far beyond the core proposition for which there is a documented 78-84% consensus — that human activity is the main driver of the observed warming.

Assuming you care about a consensus of academics working in the relevant fields, I’m not aware of any published consensus surveys on questions like whether we could prevent another 1 °C of warming with specific policies or what have you.

The science of mitigation is extremely complex and we need a serious epistemic framework capable of evaluating climate science and its long-term projections and modeling. We don’t have that framework. That is, we don’t have any validated way of assessing climate science at that level of rigor. We might have climate scientists claiming that we can dial back warming with such and such reduction of carbon emissions, but we have no way of evaluating such claims. As a society, we’re not yet operating at that level of rigor and intelligence, and climate science itself might not be operating at the needed level of rigor to satisfy such an epistemic framework.

Now, if we had the necessary exogenous framework and tools, it wouldn’t logically imply support for the totalitarian policies of the left. No one has to support anything. You might not think it’s appropriate or ethical to ban or tax meat in order to cut a 1 °C increase in GMST to a 0.5 °C increase, for example. Or to make energy more expensive for the same purpose. Energy is critical to life.

You might have all sorts of values, principles, and concerns that trump GMST, sea levels, or storm counts. That’s your prerogative as a sentient being. No one is obligated to be a climate-focused utilitarian/totalitarian. You might not think that optimizing the climate of 2100 is a valid government function. Or you might think that the premise that a 1-2 degree cooler world is in fact optimal is unfounded, or that even the idea of an “optimal” GMST is invalid.

You might also be aware that climate propaganda has been full of false and inherently implausible scenarios like the recently retracted RCP8.5, which many legacy/leftist media outlets presented as serious science without disclosing its absurd assumptions and methodology.

You might think taxing or banning meat, dairy, or hydrocarbons is a violation of important human rights, and that concerns about climate change, backed by the best current climate science, cannot justify such human rights violations. You might think that the people of 2100 do not in fact have a cashable right to a 1-2 degree lower GMST, or any regulatory claim on the people of 2026 to that end — a poorly articulated premise of much leftist discourse.

Leftists aren’t cognizant of the myriad valid reasons why someone might not care about climate change or support their coercive policy agenda. Their way of being on this issue consists of a handful of simple formulations like “climate change is a real” and “climate emergency”, with an eagerness to believe any and all claims that climate change is causing {insert harm}. No purported harms are rejected or seriously scrutinized. Legacy media routinely make false claims about climate science, from promoting or mischaracterizing junk like RCP8.5 and the Lynas 99.9 percent consensus study, to calling people who approach the issue with rigor “climate deniers”.

I’ve never seen anything like the consensus studies by Cook (2012) and Lynas (2021) — measuring consensus by having conflicted political activists read the abstracts of irrelevant journal articles scraped from an amateur keyword search, followed by deranged arithmetic. Ours was supposed to be a scientific civilization, and this is tantamount to pulling out a Ouija board and crystals. This whole topic is begging for a massive epistemic and scientific upgrade.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

Appendix A: The fingerprint solvent abstract

Here’s the abstract for “An alternative carrier solvent for fingermark enhancement reagents”:

Solstice® Performance Fluid (PF), trans-1-chloro-3,3,3-trifluoropropene, is presented as an alternative to HFE7100, methoxy-nonafluorobutane, as a carrier solvent in a number of chemical formulations used for the visualisation of latent fingermarks. The supply of HFE7100 may be at risk due to a recent European Union regulation to control global warming. Laboratory trials using split depletions and a pseudo-operational trial of 1000 porous samples have shown that Solstice® PF is a viable alternative to HFE7100 for the chemical formulations of ninhydrin and 1,2-indanedione. Other preliminary trials have also indicated that Solstice® PF can be used as a carrier solvent for the zinc toning of marks found using ninhydrin as well as the α-naphtholflavone fixative solution for iodine developed marks. Results from the pseudo-operational trial demonstrate that the number of marks detected by ninhydrin and 1,2-indanedione formulations for each carrier solvent is comparable. When compared to HFE7100, advantages of Solstice® PF include a very low global warming potential and atmospheric lifetime in addition to a higher wetting index and lower costs. This study also provides a validation study that supports the potential replacement of DFO with 1,2-indanedione.

The Lynas team read this abstract and thought it counted as input into the scientific consensus on human-caused climate change. It merely mentions EU regulations regarding global warming and touts the lower “global warming potential” of a new fingerprint solvent, a product category that cannot possibly significantly impact the earth’s surface temperature. This paper has nothing to do with climate science or climate change attribution, but even if it did, no one’s opinions of the abstract are relevant to an estimate of a climate science consensus.

Appendix B

Daniel Kammen was Editor-in-Chief of Environmental Research Letters for both the Cook, et al. (2012) and Lynas, et al. (2021) consensus studies where lay leftist activists read the abstracts of irrelevant journal articles, inferred the authors’ views on anthropogenic warming, and did some invalid paper counting and arithmetic to generate a “scientific consensus” figure.

I wrote to Kammen after he published Cook, et al. and informed him of the invalidity of the method and the fact that Cook had committed fraud.

In their paper, they stated that their raters were blind to author and journal, that they could only see the paper title and abstract. Their methodology was inherently invalid, so nothing they did would’ve mattered. In any case, they presumably thought this blindness was important because they’d heard of double-blind studies where researchers are blinded, and given that they maintained an Enemies List of climate scientists who they deemed insufficiently alarmist on their website, they anticipated being accused of bias if they could see that the authors of a given abstract were their leftist comrades or named enemies.

Well, they lied about being blind to authors and journals. They maintained a private web forum for their raters, and at least one rater openly broke blindness, saying that an abstract “smelled like Lindzen” and that he looked it up and indeed it was authored by Richard Lindzen, MIT emeritus, one of the climate scientists on their Enemies List. Since they were amateurs working from home, blindness couldn’t be enforced anyway and we wouldn’t normally be expected to believe that such a motivated team of political activists could be trusted to police themselves at home.

Kammen was evasive and would not retract the Cook, et al. paper. It turned out that he was a staunch leftist and had campaigned for and donated to Obama. Obama had tweeted about the Cook study, even adding a false claim that the consensus included the appraisal that climate change was “dangerous”, an issue that Cook’s home workers did not purport to address. Kammen had already proudly blogged about Obama’s tweet touting the study.

I hadn’t thought about Kammen in years, until he pulled this childish, maniacal stunt seven months into Trump’s Presidency:

What’s he up to these days? Well, this is his most recent social media post, four days ago:

This is extraordinary. This is something the maintenance guy at a trailer park would post. It’s gutter level, low-IQ nonsense. (And yes, the text is fake.) This is the person responsible for the viral and false 97% consensus claim. And the equally invalid 99.9% claim, and perhaps next year’s 109.9% consensus figure. He’s supposed to be a scholar of some description, yet he’s manifestly a low-information, conspiratorial, malicious maniac.

These are the people who complain about Republicans politicizing science. No, the reality in front of us is that the left politicized and corrupted science (and the humanities) for decades, comprehensively. The public is in the early stages of noticing.

Climate activism has a long history of innumeracy and gaps in baseline scientific knowledge (ask an activist how much warming is projected from now to 2100 in a moderate IPCC scenario – the flagship warming phenomenon). Some years ago, I came upon something so stupid that it paralyzed me. It locked my brain when I tried to write about it, and I let it sit for years like a Horcrux I dare not touch. Just knowing that it was out there bothered me.

It’s the opening paragraph of a 2012 Rolling Stone article by Bill McKibben – “Global Warming’s Terrifying New Math”. It’s a remarkable artifact that I’ve come to use as a test of applied intelligence and reading ability. Only one person has ever passed, and every climate scientist I’ve tried has failed, including Gavin Schmidt and Katharine Hayhoe:

If the pictures of those towering wildfires in Colorado haven’t convinced you, or the size of your AC bill this summer, here are some hard numbers about climate change: June broke or tied 3,215 high-temperature records across the United States. That followed the warmest May on record for the Northern Hemisphere – the 327th consecutive month in which the temperature of the entire globe exceeded the 20th-century average, the odds of which occurring by simple chance were 3.7 x 10-99, a number considerably larger than the number of stars in the universe.

The final, bolded clause is the test. Focus on his claim – the 327th consecutive month that the temperature of the globe exceeded the 20th-century average, on which he performs some sort of probability calculation. (From now on, I’ll abbreviate the 20th-century average as the 20C average, not to be confused with the actual value, which was 13.9 °C.)

Pause here and ponder his sentence. Do you understand what he’s saying? What does he mean by his “odds” claim? Is his statement valid? Is it true? What do you need to know to evaluate it?

Take your time, come back tomorrow, or keep reading as you wish.

First, you might’ve noticed his error in calling 3.7 × 10⁻⁹⁹ a large number (and implicitly an integer). It’s an infinitesimal number, a fractional decimal that starts with 0.00000… That’s not the core problem, so set that aside. (Note that Rolling Stone hasn’t corrected this obvious error in 13 years.)

Focus on his core claim — the 327th consecutive month that the temperature of the globe exceeded the 20C average, on which he performs some sort of probability calculation.

Let’s clarify that his span of months covers 27¼ years, running from March, 1985 through May, 2012.

He’s saying that this span was warmer than the 20C average. Clear?

That’s odd, since of course it was. That’s what warming means. If the earth warmed throughout the 20th century, then of course the late 20th and early 21st centuries will be warmer than the 20C average, unless it cooled significantly during that period.

What about his odds statement? Well, not all strings of words are meaningful, and his is not. Before we proceed, let’s ground ourselves with this graph of global temperatures from 1901-2012.

McKibben is essentially looking at this warming trend and saying “Wow, the monthly temperatures starting in 1985 are always above the 20C average, and isn’t that incredibly fishy?” His span starts at 0.22 °C above that average (March, 1985) per the NOAA global land and ocean data.

This is about as stupid as humans get.

McKibben doesn’t provide any sources or identify the dataset he used, but they’re all similar enough for our purposes. It wasn’t the NOAA data, or his span would’ve started in 1979, adding 72 months.

NOTE: All temperatures here are Global Mean Surface Temperature (GMST), a complex statistic that draws from thousands of stations and involves various corrections and weights. I’m not confident in its validity or utility as a construct, but let’s grant it for present purposes.

First, let’s note that the earth is a planet, not a sidewalk, and as such it rarely cools by more than 0.22 °C in a month. As you can see, by the late 1990s, temperatures would have to drop by at least 0.4 °C to dip below the 20C average. The graph is annual, not monthly, but it gives us the gist, and no month from 1901 to 2012 cooled by 0.4 °C or more. The average monthly change from 1901 – 2012 was 0.00071 °C of warming. (Here’s the NOAA data on Google Drive if you don’t want to build the file yourself.)

Solving for X

Back to his odds statement. What is this 3.7 x 10⁻⁹⁹ number? It’s a probability – which he incorrectly calls odds – and he says it’s the probability of every month in that span remaining above the 20C average “by simple chance”. That statement has no meaning, but let’s pause to figure out how he computed this.

For some reason he thinks monthly averages are important and he performed some sort of calculation regarding this string of 327 months. What calculation? It must be this:

So what was his x? Well, we need to solve an exponential equation, child’s play for the American man. We can take the log of both sides and work the steps or just invert and switch the exponent. Therefore:

Which equals 0.5.

When I discovered this a few years ago, I wanted to go on walkabout. We won’t have a civilization for long if this savagery is normalized. McKibben treated a planetary climate system as a monthly coin flip, such that global temperatures had even odds of landing on either side of his arbitrary baseline, even though the planet was well above that baseline at the start. If in one month the earth was, say, 0.4 °C above the 20C average, he’s saying there are even odds that the next month it would either:

1. Cool by more than 0.4 °C.

2. Cool by less than 0.4 °C, warm by any amount, or stay the same.

In reality, Option 2 was certain — the earth has never cooled ≥ 0.4 °C in any month from 1901 - 2012.

He took 0.5, treated it as an independent probability, and multiplied it by itself over and over — 326 times — thinking that he was computing the “odds” of every month in that 1985-2012 span remaining above the 20C average by “simple chance”.

That formulation has no meaning. There’s no such thing as “the odds of every month from 1985 to 2012 remaining above the 20C average by simple chance”. The earth is a planet — its surface temperature and energy balance are physical, empirical realities, it doesn’t randomly reset every month, and his span starts at a temperature that is already well above his arbitrary 20C baseline. The earth won’t shed such massive amounts of energy in a month.

It’s like asking for the odds that the earth remains a planet from 1985 to 2012, as opposed to turning into a dog or something, “by simple chance”. This string of words has no meaning. You could compute some probabilities of a monthly average – or a string of them – being above or below a baseline, but if you use a baseline well below normal fluctuation, and during a warming trend, you know the answer without needing to compute anything. In any case, there is no “simple chance” – it’s an empirical question that would be powered by empirical data and modeling. You can’t do anything with a calculator and a fixed, independent probability like 0.5 or any other value.

By “simple chance”, McKibben might mean “if not for global warming”. But the earth did warm, he started with the earth 0.22 °C above his baseline, and again the earth is a planet, not a coin. He thinks his probability calculation is new information, that it adds to the evidence for warming and cause for alarm.

McKibben found a way to take the observed warming — the recent past that we already knew about — and turn it into a “terrifying” new development. He made the past terrifying.

That is truly remarkable and highlights the profound neuroticism, emotion dysregulation, and innumeracy that drives so much leftist climate panic. This is not a scientifically serious movement or belief system. A major media outlet published this – what might be the most savagely stupid misinformation we’ll ever see, and in thirteen years has not noticed or corrected it. It’s not just a failure of baseline intelligence, but of cognitive activation. The people at Rolling Stone are not able to read his opening paragraph and understand what he did. In fact, I’m not sure they could follow this explainer.

Similarly, partisan media outlets are not able to read fraudulent or invalid climate science consensus papers and notice that they’re fraudulent or invalid. We have a broad and deep problem with getting people to read attentively across topics, especially when they’re politicized. We’re just not getting adequate {baseline IQ + attention} focused on media articles and claims. So much nonsense is flowing into our brains — collectively, we’re not operating at a sufficient level of consciousness. It shouldn’t be possible for McKibben to have published his claim, nor for it to remain uncorrected for 13 years.

Testing the Experts

I mentioned that I use McKibben’s paragraph as a test.

In 2022, I asked two climate scientist-activists a basic question: Starting now, what’s the probability of any given month’s GMST being higher than the 20^th century average?

Gavin Schmidt got the answer right straight away – 100%. He stumbled in understanding what McKibben did, saying that it was “the likelihood of the observed trend assuming no external forcing”, but no trend is needed for monthlies to not drop below the 20C average when you start 0.22 °C above it. I don’t think Schmidt realized that the period started in 1985. More fundamentally, taking an imagined probability to the 327^th power does not yield the likelihood of anything, much less the likelihood of the observed trend assuming no external forcing.

I also asked Katharine Hayhoe. I never got her to understand the question. It’s an unusual question, but trivially easy if you just take a second to think about it. It takes a certain versatility and rigor to be able to reason about novel questions and step outside the scripted cognition that can dominate our daily lives.

Worse, Hayhoe didn’t seem to know the 20C average offhand or where we were relative to it. It was awkward for me to know more about basic climate science facts than a purported climate scientist – I’m a social psychologist. The red flags on Hayhoe’s website proved prescient – she very awkwardly brags about honors she’s received, including “World’s Greatest Leaders”, “100 Most Influential People”, and even wrote “In 2019 I was honoured to be named to Foreign Policy’s list of 100 Global Thinkers for the second time” (emphasis hers).

I would be so embarrassed to disclose that anyone had named me one of the “100 Most Influential People” or a “World’s Greatest Leader” – I can’t imagine putting it on my own website. I knew Hayhoe was a political partisan committed to stoking fear and hatred over mild climate change, but I hoped she would at least understand the barbarism of what McKibben did. I wanted leftists like her and Schmidt to be the voices of reason and get Rolling Stone to correct, but I couldn’t even get her to understand basic climate science.

In any case, the full test is to present McKibben’s opening paragraph, highlighting the clause about the 327 months, and see if people can identify the problem. They’ll need to solve for his x and know the basic background fact that the earth warmed over the course of the 20^th century. It will help if they have a sense of how much the earth warmed and what monthly fluctuation looks like. It’s a fascinating test because it requires a level of attention and exogenous intelligence that people rarely apply to what they read1. Try it.

An earlier version of this article was published on Watts Up With That?

Joe Duarte grew up in copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on data science and political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

Last week, NYU activist Jay van Bavel introduced another study trying to get people to take “climate action”. The study itself is laced with false claims — which they present to their participants as facts — and I’ll go into that on Wednesday.

To frame Wednesday’s article, I want to share this essay I wrote as a graduate student in 2015. It’s an interesting throwback to a time just before wokeness swept over us, and it does a decent job of addressing the phenomenon of social psychologists being fixated on why non-leftists exist and designing invalid, rigged studies to pursue that question:

In my comment on Piercarlo Valdesolo’s excellent Scientific American guest column on our paper, I wrote up some points that deserve a separate post.

Some people come into social psychology with a political agenda – their research and careers are driven by left-wing ideology. A common pattern is to investigate how non-leftists can exist, why they believe what they do, what’s wrong with them, and how we can change them. This leads to pathologizing conservatives, and lately, non-environmentalists.

It sometimes looks like dissonance reduction on the part of researchers. If you believe your ideology is true, but look out upon the world and see that large numbers of people don’t embrace it, it can be frustrating (I’ve been there as an occasional pro-immigration activist.) You have a list of issues you think must be urgently addressed by society, yet society is not addressing them, perhaps doesn’t even see them as problems to begin with. This can create a lot of dissonance – why don’t people see what we see or think as we think? One way to resolve that dissonance is to assume that there must be something wrong those people, that there must be “causes” behind their positions other than simple disagreement, much less any wisdom on their part. So the next step is to inventory the (uncharitable) reasons why people don’t embrace your ideology, the ideology you just know is true and noble.

Jost’s system justification theory is a good example of searching for an explanation for the non-universal appeal of leftist ideology. There’s a heavy effort to find out how people can possibly justify “the system” or the “status quo”, as leftists put it. The framing is usually: Obviously it’s an unjust capitalist system, so why aren’t people revolting? How can the poor support a system that disadvantages them?

These questions and framings are loaded with ideological assumptions, and it’s noteworthy that the field has not policed this ideological bias and allows such ideological content in peer-reviewed research. Assumptions include:

• that our “system” does in fact disadvantage the poor

• that the poor (the left quartile of the bell curve?) in America are victims of injustice

• perhaps that poverty (again, the always-present left side of the bell curve) is inherently unjust and/or morally relevant

• that the poor would be better off or their interests better served in some other kind of system (socialism? communism? a 20% chunkier welfare state, a la Denmark?)

• perhaps implicitly that the rest of society would not be worse off in that system

• that our current system should be seen principally in terms of its capitalist or market elements and not by the abundant socialistic and regulatory elements that have emerged since the 1930s

• that any harms, crashes, or suboptimal outcomes are caused by the capitalistic or market elements and not by the socialistic, tax, or regulatory elements

• that some sort of materialist, collectivist utilitarianism — perhaps a Marxist variety — is how we should evaluate societies

• that social psychologists should base their research around such philosophical-ideological premises

Beyond system justification, lots of other research focuses on why people aren’t leftist in their outlook, how they can “tolerate” or “rationalize” income inequality (variance in incomes), why they don’t care about the things leftists care about, whether they are “pro-environmental” and how to make them more “pro-environmental”. Environmentalism is a new political ideology, and possibly a religion or a substitute for traditional religion, and it’s alarming that social psychologists are promoting it and trying to convert people to it. Embracing new, abstract, and somewhat ambiguous values like “nature” and “the environment” is just assumed to be equivalent to rationality or something. Environmentalist values are contested by scholars all over the place (though not so much within academia), but the field seems unaware of this, and unaware of their status as values, as ideological tenets, as opposed to descriptive beliefs about the world.

Biased measures and scales are a common outcome of this kind of bias. We have caricature scales, like Right-Wing Authoritarianism and Social Dominance Orientation, full of cartoonish items that conservatives do not actually endorse. But we report correlations that ride on variance between people at the floor on the cartoon items (leftists and libertarians) and people at or just below the midpoint (conservatives). That is, our correlations don’t actually reflect a reality of agreement with the items (in data I’ve seen, only single-digit percentages of participants score above the midpoint on these scales, and this is largely unreported in papers using these measures.) We have measures of “free market views” that are loaded with proprietary leftist terminology like “social justice”, a conception of justice specific to the left and which has no currency anywhere else.

A core issue in all this is that we have complete hegemony over the words. We basically force participants to respond to questions of our choosing, made of our words, resting on our (often ideological) assumptions. Participants have no voice other than the voice we allow them. This is a huge validity problem, and a focus of some of my upcoming work.

There’s no equivalent conservative or libertarian bias in the field, probably because there are virtually no conservatives or libertarians in the field, and if they framed their research around similar ideological agendas it would be an easy catch for a leftist field. For example, if a conservative researcher framed his research by talking about the “staggering” number of abortions, he’d be run out of town. (It’s common for social psychologists to speak of “staggering inequality”. I’m sure that any realistic statistics for income or wealth would count as “staggering inequality” to them.)

What’s more, we often see researchers declare outright that their motivation is to advance their ideology, to spark political action, and so forth. I think it’s impossible to argue that the field is not biased when researchers declare themselves to be political activists and that their research is an outlet for said activism. Again, the Jost Lab is the canonical example (but not the only one) – it would be hard to distinguish it from a left-wing political action committee or lobby, given the declared intentions and ideology of its researchers. You can always find explicit activism there. For example, Sharareh Noorbaloochi’s lab page reads, in its entirety:

“Sharareh’s research focuses on behavioral and neural bases of moral-political attitudes and behaviors. She is currently studying the root causes of moral exceptionalism in the context of foreign policy disputes. Specifically, she investigates how political ideology, moral orientation, and system justification motivation shape moral exceptionalism and intends to use the findings to develop wise interventions aimed at alleviating this barrier to global justice.”

I take it that “moral exceptionalism” is the position that it’s possible for one party in a dispute to have greater moral standing than another party, for example that the United States or South Korea might have greater moral standing than North Korea, a country ruled by a cult dictator, full of forced labor camps and people so poor that they are several inches shorter than their South Korean brethren.

Whether moral exceptionalism is defensible or not is a philosophical position. This researcher has not only ruled on the issue, but has decided that it is a “barrier to global justice.” Her research is geared around developing “wise” interventions to alleviate this “barrier.”

She also says she wishes to study the “root causes” of moral exceptionalism. What if the root cause is people believing that it’s wise? What if the root cause is simply disagreement with the researcher? Why is a mundane philosophical position assumed to have “neural bases”?

Why isn’t she investigating the “root causes” of disagreement with moral exceptionalism? What’s the root cause of that? What are the neural bases of wanting to increase taxes? What’s the root cause of being anti-business? What’s really behind being a Methodist?

This researcher has already decided that holding a particular position that she disfavors has a certain class of “causes”, including behavioral and neural bases. She has pre-emptively shrunk reality, the reality that she will allow herself to see. She will not find a root cause of wisdom, merit or sincere, reasonable variance on philosophical matters and values. Rather, she is extremely likely to find what she is looking for, if she’s allowed the use of invalid and biased measures like Right-Wing Authoritarianism. If people who endorse moral exceptionalism tend to register a 3 on a 1 - 7 scale of cartoon items (a 3 is mild disagreement), while people who reject moral exceptionalism cluster around 1 or 2, we can expect to see “Right-Wing Authoritarianism Predicts Moral Exceptionalism”. (In fact, because the way the math of correlation works, such a finding could easily be reported even if almost no one endorsed moral exceptionalism.)

I won’t even linger on the ocean of ideology resting underneath her use of “global justice.”

Science requires us to be more sober than this. We can’t go in having decided already what kinds of causes must be in force. The above example is pervasive in social psychology – the recurrent attempt to attach “causes” to various non-leftist philosophical or political positions. Notably, contemporary leftist thought seems to attach nefarious “motives” to disagreement with leftism. It’s a good protective immune system for an ideology to have, to pre-emptively marginalize and de-legitimize dissent as corrupt or ignorant and thus deter one’s members from closely examining alternative schools. I assume leftist ideology is not the only ideology with such an immune system regarding dissent – it might be a recurrent feature of ideologies as such. It would make sense. In any case, a valid social science needs to immunize itself from this sort of ideological embedding.

Regarding the Jost Lab in particular, I think it would be difficult to characterize its activities and methods as valid social science. I’m not sure how we would craft a credible defense of the above example of political bias – what sort of argument would redeem it as not biased after all. In his Edge piece in response to Jon Haidt’s SPSP talk on political bias, Jost disputed that the field is biased, saying:

“This is because we, as a research community, take seriously the institutionalization of methodological safeguards against experimenter effects and other forms of bias.”

The field has no safeguards against political bias, as least none that are branded as such. No such safeguards have scoured the Jost Lab of its deep political bias, as the above example illustrates. As far as I’m aware, Jost has not instituted any efforts to strip his lab of such biases, or to otherwise reduce them. His Edge piece takes leftist ideology for granted, instead of treating it as one ideology among a broad constellation of alternative frameworks. For example, he treats “social justice” and “environmental sustainability” as descriptive categories of research, along with mental health and intergroup relations, where “the best scientific minds have found liberal ideas closer to the mark.” These are not descriptive categories, but rather are leftist values and science has in way validated their truthiness or utility. They’re also highly abstract concepts that entail assumptions that not all scholars will grant – in other words, they’re question-begging.

It seems to be in the nature of ideology to convert ideological tenets and value judgments into descriptive facts/concepts in the mind of the ideologue. Here I don’t think Jost necessarily sees his ideology as an ideology, or understands how contingent and optional concepts like “social justice” and “environmental sustainability” are. If the Jost Lab can’t get their heads around the fact that leftist ideology is an ideology, and aren’t able to zoom back from their ideological framework and see the border between descriptive facts and values, they’ll struggle to conduct valid scientific research in political psychology. If that’s the case, they should shut down the “lab”, perhaps reconstitute it as a left-wing political lobby or think-tank independent of NYU. If they’re not prepared to shut down, they’ll probably want to consider major bias-corrective measures. One measure would be to actively recruit non-leftist researchers, since all their current and former researchers appear to be staunch leftists, even self-proclaimed activists. A conservative or libertarian (or two) in the Jost Lab would make it much more difficult for biased research to come out of it, especially if they were included on every paper. In this case, I think such inclusion would be required.

Lastly and similarly, I was also stunned to see SPSP diversity travel award recipients — most of whom were not underrepresented minorities — start their bios with statements like “I am an activist.” Not a scientist. An activist. We know what kind of activist they will be, what ideology they will be trying to advance. And it was depressing to see virtually all the minorities focus their research on prejudice and stereotyping against their own minority groups. It’s like as soon as a minority steps into the field, they go into their assigned corner and conduct ideologically-biased and approved mesearch on prejudice. This marginalizes the very few minorities we have, and somewhat weakens the benefits of diversity, since they’re not attending to core social psychology research and the cultural biases therein.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

I've posted the R and SPSS code and data to conduct the factor analysis on Lewandowsky, et al.’s fraudulent 2012 paper “NASA Faked the Moon Landing—Therefore, (Climate) Science is a Hoax: An Anatomy of the Motivated Rejection of Science”.

Ten identical item factor loadings?

Their online study purported to measure belief in various conspiracy theories, asking participants about 14 different conspiracies. Incredibly, the authors reported the same factor loading for all ten items in their main conspiracy factor: .742.

This means that the loadings are false, since they cannot be identical — it's not mathematically possible. The authors never disclose the actual factor loadings.

With my code, you can generate the actual factor loadings. It turns out the conspiracy belief latent variable reported by Lewandowsky, et al. doesn't exist. Few of the items load heavily on any factor, and participants generally didn't endorse the conspiracies. Here are the results:

The paper is false and should be retracted — the authors reported false, identical, and inflated factor loadings.

How could a journal publish a table full of identical item factor loadings? It’s like a correlation matrix full of identical three-digit correlations — impossible. I haven’t found a single case of anyone ever doing this. It’s an error far too obvious to get past reviewers.

Well, the authors inserted this footnote under their table of false loadings: “There were two composite manifest variables for conspiracist ideation. The same loading is shown for all items that entered into a given composite variable.”

That’s not an option. We can’t give the same loading for every item, since they’d necessarily be false and we can’t present false data in a scientific journal article. (“Manifest variable” is a term some foreign researchers use for what Americans call measured or observed variables, or just the item scores. Lewandowsky is Australian.)

That was an attempted Jedi mind trick, to boldly, casually say you’re doing something that you absolutely cannot do and which makes no sense whatsoever. Apparently, it worked on the reviewers.

In their article text, the authors also said: “Responses to items loading on each conspiracist-ideation factor were summed, so that two composite manifest variables were created.”

That’s false, since several of the items didn’t load on a conspiracist ideation factor. As we see above, the moon landing, New World Order, Oklahoma City, and SARS items didn’t load well on any factor. Yet the authors included them in their bizarre “composite manifest variables”. So their statement “responses to items loading on each conspiracist-ideation factor” is false. And note that they never disclose those loadings, which enabled them to conceal the fact that several items, including the one in the title, didn’t load on any factor.

Note also that hardly anyone endorsed most of the conspiracies. So even the .497 loading for the Princess Diana conspiracy is meaningless since only 2.2% of participants endorsed it. Descriptive data trumps inferential statistics — academic researchers and reviewers often get distracted and tricked by inferential statistics, SEM models, factor analysis, etc. without contemplating the descriptive data first. Lewandowsky didn’t report any descriptives in the paper, so readers would have no idea that hardly anyone endorsed these conspiracies.

This was incredibly bold. I’ve never seen or heard of anything like it. A table of identical, false factor loadings just staring right at readers, with a meaningless string of words used as a Jedi mind trick… And never reporting the actual loadings.

The title is false

Moreover, the titular link between believing that the moon landings and climate science are hoaxes doesn't exist — the authors fabricated it. In the sample of 1,145 participants, only ten (10) participants thought that the moon landings were a hoax — effectively zero — and most of those ten did not believe that climate science was a hoax (though we can't do anything with 10 of 1,145 participants anyway – it wouldn't matter if all ten thought climate science was a hoax).

Thus, there's no link between the two beliefs in this study — there wasn't even any significant data/endorsement for the moon landing belief. The authors even asserted a causal structure for this nonexistent link, with the moon landing hoax belief driving the climate hoax belief: NASA Faked the Moon Landing—Therefore, (Climate) Science is a Hoax.

Note that this false and fraudulent study was widely reported by credulous media at the time, including Scientific American and the Guardian. It drove a narrative that climate skeptics were broadly irrational and believed in all sorts of crazy conspiracies, particularly that the moon landings (plural — there were six) were a hoax. The Association for Psychological Science (APS), didn’t just publish this fraud – they promoted it in their Monitor magazine. No one could be bothered to think critically, to properly review it, or to even look at the data.

“Science” in this era, especially social science, is extremely unreliable. Two reasons stand out:

1. Leftism. Leftist ideology saturates academia and researchers are motivated to advance leftist narratives and power, and to derogate, mock, and even dehumanize non-leftists, particularly conservatives. (Though academics have a binary model of the intellectual and political landscapes, viewing all non-leftists as conservative or right-wing.)

2. There’s little independent thinking and validation going on. A peer-reviewed journal published a table of identical item factor loadings. The reviewers fell for the Jedi mind trick. No one even noticed that the title was false. (Peer reviewers don’t look at the data for studies — no one does, even just to see that it exists.)

Other issues

Who were the participants?

We know nothing about the participants of the study. We don’t even know their nationality. Or their sex, age, or any other basic demographics. This is as unprecedented as a table full of identical, false factor loadings. I’ve never encountered a study that didn’t even disclose the sex or age breakdown of their sample.

Lewandowsky was in Australia at the time and this was an online study. He was active in arguing with climate skeptics down under and I assume most participants were in Australia and New Zealand, but we simply don’t know.

The data was altered

Two different data files were posted, but for the same 1,145 participants. All the survey questions used a crude 1-4 scale1 of disagreement to agreement, and one of the files had no ones for one item and no fours for another item. At least one of the files — the one with the missing ones and fours — was altered, possibly both. Summary here.

Something is fundamentally off with the data, since endorsement of the moon landing conspiracy was far too low at 0.8% to be real. In the US, typical rates are 6-8%, and Australia has bounced around 5-10%. The paper’s substantive claims are false, including its very title, but this study wasn’t usable to begin with. Mystery participants, an invalid response scale, poorly worded items, deleted participants, and altered data = there’s nothing we can do with this. As I’ve said before, non-academic organizations like Gallup and Pew operate at much higher standards of scientific rigor than academic researchers.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

The Free Press is 99.3% reliable, while the Daily Wire, Fox News, and the Washington Post are 99.1%, 98.2% and 82.4% reliable, respectively. These are the percentages of headlines and articles that did not contain a false claim, according to the Media Reliability Project’s systematic study. The Post’s reliability falls to 62% for its fact checks specifically, and 44% for fact checks unfavorable to President Trump — more than a hundred fact checks claiming that Trump said something false were themselves false, while thousands turned out to be invalid, addressing opinions or claims that cannot be fact-checked. Grok 5 and Gemini 4 were used to confirm the findings.

I’m developing a project to systematically assess the reliability of major media outlets. Surprisingly, no one has done this. The above fictional passage is an example of the findings we should be able to make.

We don’t have any data on the reliability of the New York Times, ABC News, the Free Press, CNN, Politico, Fox News, the Washington Post, the Daily Wire, etc. There’s nowhere to go to see a rigorous assessment of their reliability. There’s no report (preferably live/updated). There’s nothing.

Legacy/leftist media in particular make lots of false claims, and while those claims are regularly exposed on social media, no one is comprehensively sampling and assessing their headlines, articles, and TV segments for false claims and tabulating that data. Without such a rigorous approach, there’s no way to hold them to account or to establish which outlets are more reliable.

What do I mean by reliability? I mean the inverse of their false claim rates. This is fundamentally about false claims. Let’s focus on headlines specifically for a moment. An outlet’s reliability with respect to headlines would be the percentage of their headlines that are not false.

The project will also include full coding of article text and TV segments, but headlines are worth assessing separately because headlines are all most people see and coding headlines is a good Phase 1 study before tackling entire articles.

Right now, unreliable legacy/leftist media outlets are favored by search engines, Apple/Google News, Wikipedia, and emerging AIs, while non-leftist outlets — which I predict are more reliable — are marginalized. This erodes our connection to reality — false claims are laundered, legitimized, and heavily repeated while inconvenient facts are suppressed.

This project will yield a treasure trove of scientifically rigorous, unimpeachable data, with which we can address claims that legacy/leftist media are bulwarks against “misinformation” (claims embedded in public school curricula on “media literacy” and academic misinformation research), pressure media to correct false claims and biases, and tackle the problem of unreliable leftist media being used as reliable sources for web search, AIs, Wikipedia, etc. before it’s too late.

To properly make the case that legacy/leftist outlets are unreliable and to upgrade the quality of our information sources, we need hard data, rigorously and transparently collected and coded. We can cite plenty of examples of false reporting, but to prove which outlets are more or less reliable, we need systematic data collection covering leftist and non-leftist outlets.

If you think false reporting from major outlets is exceptionally rare, well let’s just take a look.

Here’s a false headline from the New York Times. It’s still up, uncorrected:

The executive order does not bar “transgender student-athletes” — a category that includes women — from women’s sports. It seeks to bar men from women’s sports, whether they’re “trans” or not. The key passage directs the Secretary of Education to:

prioritize Title IX enforcement actions against educational institutions (including athletic associations composed of or governed by such institutions) that deny female students an equal opportunity to participate in sports and athletic events by requiring them, in the women’s category, to compete with or against or to appear unclothed before males. (bold added)

Therefore, the headline is simply false in two respects:

1. The category “transgender student-athletes” includes women (who “identify as” men, “nonbinary”, or a newer construct) — they would not be barred from women’s sports, since they’re women.

2. Men would be barred regardless of whether they identify as trans, so again “transgender student-athletes” doesn’t map to the EO. For example, the two male boxers who beat up women at the 2024 Paris Olympics aren’t trans — they’re just men. Trans and “gender identity” are new leftist constructs — non-leftists don’t need them for anything and the EO doesn’t need or rely on them.

Virtually all legacy/leftist media outlets published versions of this false headline claiming that the Trump administration was banning trans athletes from women’s sports, or even from sports as such — it was a systemic collapse of journalistic standards, a collapse that extends to coverage of gender ideology more broadly. These headlines would all be captured by the reliability study as false.

False impression = false headline

Here’s another example:

I chose this because it’s an interesting, more complex case. Technically, each claim in the headline is accurate, but it’s false in suggesting that she was sentenced for ending her pregnancy. She was sentenced for burning her fetus and pled guilty to concealing or abandoning a dead body. (She aborted her child during the third trimester.) By contrast, NBC News furnished a clean, honest headline:

The principle we could apply here is: When a headline reports an arrest or sentencing in conjunction with an act that is not the act for which the person was arrested or sentenced, the headline is false because it misleads readers to believe that the act in the headline is the basis for the arrest/charges.

I think we can convincingly argue for this principle, and nest it in a broader principle: Does the headline give readers a false impression? From that foundation we can build subsidiary principles like one regarding how arrests are reported. We could even run some studies to establish that readers get a false impression from certain headlines, but in the above NYT example I think any reasonable person would agree that no study is necessary.

The NYT biased it even more by calling an adult a “teen”, which most people will assume is a minor — many readers will not see the sub-headline giving her age, depending on the platform or news app in which they encounter the headline.

Readers who see only the main headline will come away with the impression that “red” states are jailing teens for using abortion pills, presumably very early in their pregnancies (which is when abortion pills are prescribed). That’s wildly different from the reality here of a young woman who aborted her child during her third trimester and burned and concealed the corpse, the act for which she was jailed.

ABC: The principle outlined above can be broadened to capture a lot of biased or false headlines. The core structure here is that A does B because of C. What legacy/leftist media often do is conceal C and replace it with something generic or even irrelevant. Here A is [Nebraska], B is [sentenced this woman to 90 days of jail], and C is [for burning and concealing the corpse of her third-trimester fetus/baby]. Obviously intending to deceive their readers, the Times concealed C and replaced it with the fact that she took abortion pills.

This corruption of the basic ABC structure is a common media tactic and underexposed.

We should also pause here to note that the New York Times is plainly corrupt and should not be cited or treated as a credible news source. And yet, it is treated as credible by Google search, Apple News, Wikipedia, the various AIs, etc. The goal of this project is to fully expose such organizations with scientifically rigorous data so that they can be marginalized and replaced with more reliable sources.

The Denominator

Since I’m operationalizing reliability as the percentage of headlines, articles, or segments that do not make false claims, a key question is the choice of denominator — the set of headlines to include and code as false or not. Since this project is focused on political bias and reporting, only politically relevant headlines will be included. This would include headlines mentioning a politician, party, branch of government, elections, political issues, and so on. The criteria will be refined over time based on what we learn. Leftism is unique in holding that “everything is political”, so we sometimes find leftist ideology and narratives embedded in sports, entertainment, or even cooking articles, but the vast majority of the reporting we’re interested in will be in the normal news and politics beats.

We can also refine the datasets to answer more specific questions. For legacy/leftist outlets, I predict that their reliability will be lowest for headlines and articles that mention Trump or Republicans, or intersect with leftist gender, race, or climate ideologies. For conservative outlets, I predict that their reliability will be lowest for headlines that mention Democrats and leftists (though I think we’ll find exceedingly few false claims at outlets like the Daily Wire and Free Beacon — I’ll be impressed if you can find one in an hour of searching).

For centrist outlets like the Free Press and Quillette, I have no pattern predictions, though I expect them to do well in general. Consistent with the opening passage, I predict that conservative, centrist, and libertarian outlets will prove significantly more reliable than legacy/leftist media, but perhaps I’m wrong.

Other efforts

NewsGuard

No one is systematically assessing media outlet reliability. NewsGuard, a leftist NGO and censorship effort produces “Trust Scores” using a secret subjective rating process by a handful of activists. They claim to apply various objective criteria, but they don’t disclose their actual process, rubrics, sampling methodology, or any details for a given outlet’s scoring, nor do they release their data, so there’s nothing we can do with their “Trust Scores”. (Their process might be so informal that there is no “data” per se.) In fact, they don’t even disclose the scores — you have to pay to see them (though they’ll sometimes disclose a score in their newsletters).

For conservative outlets, they seem to home in on whatever leftists are calling “misinformation” at the time and rate the outlets poorly if they promote the purported misinfo, even when the topic is a contested, complex scientific and policy issue like COVID mitigation strategies. Ironically, NewsGuard is an enforcer of misinformation – they swung into action to harm media outlets that shared or promoted the COVID lab origin theory (which is almost certainly correct) and they assigned an unqualified lay activist to “fact check” the Great Barrington Declaration website. NewsGuard abandoned its purported role as a media watchdog and took partisan political stances in favor of totalitarian COVID policies.

For leftist outlets, they simply ignore their notorious false claims – e.g. the Trump-Russia collusion hoax, the Hunter Biden corruption emails/laptop billed as Russian disinfo, COVID lab origin as a debunked conspiracy theory, denying that children are subjected to mutilating “gender” surgeries (over 6,000 kids so far), COVID vaccine efficacy, claims that all “transgender athletes” are being banned from women’s sports, that atmospheric CO2 is toxic, several hoaxes attributing the slaughter of Gazan civilians to the IDF, unscientific subjective opinions like a state of climate “crisis/emergency” treated as journalistic background facts, etc.

For example, NewsGuard gives the notoriously unreliable leftist outlet PolitiFact a perfect 100 Trust Score. Even on grave matters like the surgical mutilation of children, Politifact exhibits no trace of rigor, truth-seeking, or any effort to mitigate their leftist bias – they claimed there were only two examples of American children receiving such surgeries. (Their “fact check” is still uncorrected, as are hundreds of their other false fact checks.)

NewsGuard’s co-founder even pushed the false claim that the Biden emails/laptop was Russian disinfo. So we see again that NewsGuard is not the impartial watchdog they claim to be — they’re part of the broader leftist/Democratic Party machine and their beliefs about what is “misinformation” at any given time are exactly whatever leftists are calling misinformation at that time, whether it’s COVID lab origin, the Biden laptop, or the view that lockdowns were net harmful, unwise, or unethical (the last being a philosophical and political appraisal, not a falsifiable matter of fact, but that didn’t stop NewsGuard from calling it “misinformation”).

Critically though, it’s not possible to validly assess a media outlet’s reliability without using scientifically valid sampling of the outlet’s reporting – either gather all relevant headlines and articles for a given period and rigorously assess them for false claims or take a large random sample of those headlines/articles. NewsGuard doesn’t do that — they look for whatever leftists are calling misinformation at any given time, focus on that handful of topics/narratives, don’t disclose their detailed methodology, then generate a subjective opinion rating of the outlet. That’s not anything, and there’s nothing we can do with the results of such recreations.

We don’t know the specifics of their methodology or if they even have a formal methodology, but we know they’re not coding thousands of headlines/articles and they don’t look for leftist misinfo. Since they’re a leftist group, they wouldn’t be able to competently rate media reliability. Their staff will have the same false beliefs as the staff at other leftist NGOs and media outlets and the same authoritarian leftist epistemology that would look to a random, two-year-old government webpage to answer a scientific question like whether masks reduce COVID spread instead of looking directly at scientific evidence. Would NewsGuard staff catch the false NYT headlines above? Almost certainly not, which means they cannot rate media reliability.

Media Research Center

The Media Research Center (MRC) is a conservative media watchdog. They do good work assessing a few forms of leftist bias in media and in aggregators like Apple News. For example, they reported that in January 2026, Apple News promoted 440 stories from left-leaning outlets and zero stories from right-leaning outlets.

However, the MRC doesn’t assess the reliability of media outlets. They have no reliability data for the various left-leaning, right-leaning, or centrist outlets. What if the left-leaning outlets are more reliable? Since MRC can’t show that the NYT, Washington Post, and the Guardian, for example, are less reliable than the Daily Wire, Fox News, and the Free Beacon, their complaints about bias from the likes of Apple or Google are somewhat weakened.

The issue is that the decisionmakers at Apple and Google likely believe that their favored legacy/leftist outlets are more reliable than non-leftist outlets. Since no one has stepped up to do the rigorous, comprehensive research it takes to determine the comparative reliability of the various outlets, leftists have been able to get away with simply asserting that their outlets are more reliable. (They’ll also contend that leftist outlets like the NYT or Washington Post are not in fact leftist or biased, another question that can be settled with rigorous research.) They might even cite invalid, unscientific, secretive, opinion-based ratings by leftist NGOs like NewsGuard and Media Matters, or fraudulent research by leftist academics.

Since neither MRC nor anyone else has rolled up their sleeves to rigorously assess media reliability, we can’t refute their claims and institutional leftists have been able to carry on as though the New York Times, Washington Post, Guardian, et al. are reliable and “mainstream” bastions of journalism. It’s long past time to put these beliefs to the scientific test.

Yes, anyone claiming that leftist outlets are more reliable is also unable to support their contention with rigorous data. For example, Wikipedia didn’t commission a study to generate their Reliable Sources list — no, it’s based on a “consensus” of editors, i.e. the opinions of leftists. (And any study they might commission is unlikely to be valid.) Google, Apple, Anthropic, et al. would have the same difficulty objectively measuring media reliability that NewsGuard does — the people involved would likely be leftists and they wouldn’t be able to do the job. (A good test would be whether they’d notice the false NYT headlines above without prompting or clues.)

However, the fact that these companies and Wikipedia haven’t demonstrated that legacy/leftist media is more reliable than non-leftist outlets doesn’t stop them from favoring them. It’s not fair that the burden is on others to prove them wrong, but life isn’t fair and we do what we must.

AllSides

AllSides uses a handful of balanced partisans to rate the left-right bias of various media outlets. They don’t assess reliability or false claims. They seem like a smart, well-intentioned team, but ultimately bias should be objectively measured, not voted on by a handful of people. My project aims at measuring reliability first, but it will also give us a treasure trove of data that we can use to objectively measure an outlet’s bias. For example, see these NYT headlines:

Both men were former US Senators and both died in the same year (2024), so it’s a tightly controlled example of bias at the Times. This specific type of bias — derogation of the dead in headlines depending on their political affiliation — can be straightforwardly assessed by objective methods. We’d gather all headlines on the deaths of political figures, code the political orientation/affiliation of the fallen, and code the headlines for derogatory content. We’ll probably find opportunities to further break down derogatory content into subcategories, and we could also look at praise and positive content (e.g. milestone headlines — “first X to achieve Y” as with Senator Lieberman).

Here’s an example of what I term Front Page Bias, a form of aggregate bias in front page headlines.

Shooting a rare duck? It would be hard to justify putting that story at the top of their homepage, and the whole package of headlines is obviously extremely biased. These outlets know that most people only see the headlines of stories, and they’ve optimized their homepages so that skimmers will see a plethora of derogatory headlines about Republicans/Trump, even a prediction that Trump’s “purge” will cause another 9-11… (This screenshot is from February 5, 2025.)

We can objectively measure Front Page Bias, along with many other forms of bias.

Closing

The bottom line is that American media makes lots of false claims, and this needs to be captured and documented. A civilization like ours should have reliable media. We should also know exactly how reliable our media outlets are. This project will be transparent and open to the public: the methodology, rubrics, AI tuning, and data will all be open source. Every single headline, article, and TV segment included in a rating will be available, along with the coding. If something was coded as false, the explanation and nature of the false claim will be right there for everyone to see.

There will be a page for every outlet, a dynamic report with interactive dashboards. This is critical — we need to be able to point people to the complete report and data for any outlet, make it a click away1.

Coding headlines and article bodies for false claims is somewhat harder than it might seem. There are tough epistemic issues to work out in adjudicating that something is “false”. I started developing this project on the assumption that it would require well-trained human raters and I’m now exploring how we can automate as much as possible with well-trained AIs. Regardless of how much AIs are able to do, this project will be a fantastic challenge in applied epistemology. We’ll operate at much higher standards of rigor and validity than academic “misinformation” researchers — it will be good to demonstrate the contrast.

I’ll be working on the project methodology and funding for the next few months. Systematically assessing media reliability seems like the most obvious thing in the world to me and it’s confusing that it hasn’t been done. We need to be as well connected to reality as possible, because reality is where we live. This project aims to strengthen that connection.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

My empirical work mostly focused on the emotion of envy. I have a lot of data that I didn’t publish because I was frozen by a methodological crisis. I started thinking about a number of validity issues after working on our BBS paper (PDF), and I decided that I wouldn’t publish anything using only student samples or the ridiculous Amazon home workers platform (Mturk). I'm as hard on my own methods as I am on everyone else's, and I don’t want my name on anything false or invalid. In retrospect, I think the recalled envy studies were valid and worth publishing — that the participants were college students doesn’t invalidate their recalled experiences.

Eliciting envy

Recalled experience was much more useful than attempts to elicit envy with canned inductions.

A recalled experience is what it sounds like – ask the participant to recall a time when they experienced envy toward someone and have them write briefly about it.

To elicit a new envy experience during a study, I've had people read various passages or articles. I stopped using student samples a few years ago, but I have lots of student data from before, and with ASU students I had crafted fake student newspaper articles about a particularly affluent student (sex matched).

In looking at envy and anti-Semitism, I used passages that report some of the ways in which Jews excel, from higher mean IQ scores, to household incomes and Nobel Prize take rates.

The basic structure with these inductions is that you have the participant read the article and then you're looking for envy downstream, by direct measures (e.g. "How envious do feel right now?" sandwiched within other emotion items like anger, happiness, etc.) or oblique measures (mostly the kinds of things discussed by van de Ven, et al.)

I've never seen a lot of envy with the canned inductions. Maybe 10 to 20 percent of the sample will report any level of envy in that context.

However, with the recalled experience method, most participants will have something to say about a past envy episode. This method requires trained coders to extract whatever dimensions you're interested in (or you could try AI), but it gives you much richer, and I think, more valid, information.

In fact, I wouldn't assume that "envy" is commensurable across these methods. Meaning, that what we're calling envy in one method may not be the same thing that we're measuring in the other. The people who report envy after reading about an ASU student, or Jews, might not be the same people, in a sense, as those reporting vivid envy episodes in a recall exercise. That is, the emotional experience might be substantially different, and the personality predictors different as well.

One of the most important lessons Lani Shiota taught me is the benefit — and sometimes the absolute necessity — of measuring emotions without referring to those emotions by name. ASU provided excellent evolutionary psychology training. Well, I criticize the evo psych framework sometimes, but it makes us better social psychologists than we would be without it – even if we don't fully agree with the framework or some of the hypotheses generated by it. The evo psych perspective on emotions looks at their function, or their fitness value in the ancestral environment, and so when we want to carve nature at its joints, we don't want too many modern words and abstractions in the way.

Now, I did use the term envy in asking people to recall an episode. I didn't say "Think of a time when someone else had more than you did, or had something you badly wanted, and it made you feel bad." Rather, I asked explicitly for an envy experience. However, the open-ended nature of the recall method gets you much closer to carving nature at its joints. You see a broad range of elicitors and situations. You see the role of sex/gender in ways that would be hard to pick up with canned inductions. You're not as restrained by your priors. (Yes, there's a potential trade-off with effect hunting, cherry picking, multiple comparisons, etc., but I think those can be managed. Exploratory research is extremely promising and should be more common.)

Sibling Rivalry

I never thought about this, predicted it, or had any hypotheses about it. It simply never occurred to me. I'm the oldest of three, and the only boy. We're evenly spaced about six years apart. I have no experience with sibling rivalry — I've never been in competition with my (much younger) sisters, and have never resented anything they've done or achieved. I'm a low or zero envy person in general — perhaps another reason to prefer the recall method and be open-minded.

Sibling rivalry is real. I was struck by some of the participants' accounts.

The most striking pattern is that some women really hate their sisters. It was heartbreaking to read – there's real pain out there, people suffering greatly from the achievements or life status of their siblings, especially their sisters.

One question in the recall exercise asked people what they wished had happened to the person back then, or what they wished they could have done to the person. One woman reported that she wanted to "beat her bloody", referring to her sister. The circumstance that evoked the envy was that her sister had a "good husband" and a nice house. Her sister hadn't actually done anything to her as far she reported — just having a good husband and being affluent (perhaps because of her husband.) Well, that might be biased on my part. She might maintain that her sister had done something to her — it just may not look like a thing that was done to her to a man like me.

This probably highlights some of the pressures and expectations women in our culture place on themselves and/or we place on them. Some women are defining themselves through the men they marry. Perhaps that's too strong — maybe it's more that finding a good husband is a major box checked for some women, and perhaps more time-critical than the converse for men. I think this would be much less true with academic women, but I suspect it's more intuitively accessible to them than it is to men. Women know women.

This is actually a good example of the value of multidimensional diversity in any social science or branch of research psychology. We'll be limited in our ability to understand major swaths of human motivation if we don't experience them ourselves or come from the requisite culture. The BBS paper focuses on political diversity, but I increasingly think other dimensions are just as important — we need a lot more black people, Latinos, Natives, people from rural communities, people who have served in the military, people from non-Western and non-affluent cultures, and probably more masculine men. (I don't think "men" and "women" are necessarily sufficient categories. Semi-relatedly, gays are not underrepresented in social psychology — they're overrepresented (the percentage in our field is greater than their percentage in the general population).)

There will always be selection biases. Human populations vary on all sorts of dimensions, including interest in things like social psychology or football. For example, maybe ex-military types will be less interested in being social psychologists (though I think we might be surprised — understanding human behavior is somewhat central to military life, and the nature of leadership and performance are constant areas of focus.) I don't think a good social science can be so culturally specific (in this case, white urban leftists, overwhelmingly) — I think the very nature of social science requires breadth and diversity on multiple dimensions. The out-of-nowhere sibling rivalry discovery made me think about this again, how my personality, my background, my birth order, and my sex had blinded me to a major envy elicitor...

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

The National Institutes of Health (NIH) funds a large number of ideologically-driven, scientifically invalid grants, e.g. one that looks at the role of “microaggressions” in driving black people to smoke more marijuana and a new grant for a “misinformation” researcher who treated COVID lab origin as misinformation, as well as the statement “Democrats are using the coronavirus situation to harm President Trump.”

This year, I was a senior advisor to NIH Director Jay Bhattacharya, tasked with building a framework to eliminate all this ideologically-driven junk science from the NIH portfolio. Unfortunately, Jay mysteriously ghosted me from Day 1, wouldn’t respond to my messages or any grant examples, and deleted the framework summary I sent him. I have no explanation or theory for his behavior, and the result is that NIH will not be adopting a framework to eliminate ideology from scientific research, and without such a framework they won’t be able to identify ideologically-compromised grants or defend their decisions in court.

In response to my Daily Caller article about my experience at NIH, NIH Director Jay Bhattacharya and his deputy Matthew Memoli published this article in the Spectator, touting how they “cured DEI at the National Institutes of Health”.

They claim that activist courts are blocking them from terminating “DEI-focused” grants, repeating the false claim that Jay first made when he responded to my article on X.

The court injunction he referred to is from Thakur v. Trump. The judge did not block NIH from terminating bogus grants. Her ruling applies only to grants “terminated by means of a form termination notice that does not provide a grant-specific explanation for the termination”.

That’s it. That’s the impediment. Obviously, NIH can provide specific reasons for all its terminations. For example, for the “microaggressions” and marijuana grant, NIH could say something like:

“Microaggression” is a proprietary leftist construct that asserts that mundane speech like a white person denying that he’s racist is a “microaggression”. Thus “microaggressions” don’t exist except in the minds of leftists who construe them into being. Such ideological constructs cannot be the basis of a valid scientific study.

Done.

A rigorous and neutral Clean Science framework would make it easier to identify and articulate the specific issues with bogus grants. For example, it would explicitly cover the phenomenon of embedding ideology into grants, like “microaggressions” in the above case. It would feature a clean, neutral definition of ideology, such as “any construct that represents a purported injustice or oppression”, especially if the purported oppression is contested.

Jay has repeatedly claimed that he can’t do anything because of the courts, but that’s just false — open the injunction and see for yourself. From his behavior, we can infer that he doesn’t want to eliminate (leftist) ideology at NIH. It’s also possible that he believed false claims from NIH staff regarding the court injunctions, but that wouldn’t explain his incredible ghosting behavior while I was at NIH.

DEI, DEI, DEI

In his Spectator op-ed, Jay framed the issue as being about “DEI-focused grants”. This mirrors the boilerplate in the grant terminations and the Priorities Directive that Memoli issued in February as Acting NIH Director.

Two problems:

1. Not all of the ideologically compromised research is based on DEI ideology — e.g. the Orwellian “misinformation” work has nothing to do with DEI. Leftist ideology is much bigger than DEI.

2. NIH and the Trump administration more broadly made the false claim that “DEI studies are often used to support unlawful discrimination on the basis of race and other protected characteristics”, which blew up in court1.

What’s happened is that instead of squarely and systematically addressing the way in which leftists have undermined science at NIH, NSF, and academia, the administration locked onto DEI only, which is too narrow. In so doing, they ultimately overloaded the term and are calling everything “DEI”, which is undermining them/us in court. (Not just one court, but several.)

Two months before the false claim that “DEI studies are often used to support unlawful discrimination on the basis of race and other protected characteristics” was exposed in the Federal District of Massachusetts, I deleted it from the NIH Priorities Directive.

This was in April, weeks before I started at NIH. Jay sent me the directive and asked me for help revising it. I noticed the false claim right away and cut it, among many other edits and revisions. But Jay didn’t swap in my fixed version of the Directive, so in June NIH was needlessly embarrassed in court because the old February Directive with the false claim was still in force.

If it’s not clear, the claim is false because the relevant NIH studies are not generally used to support unlawful discrimination. For example, studying whether “microaggressions” drive black people to smoke more marijuana doesn’t support unlawful discrimination.

Same with the “anti-racist” grant at San Francisco State that takes minority teens to parks in a deranged bid to reduce their telomere erosion (the redundant DNA sequences that erode as we age). You could argue that the study discriminates against whites by not including them in their samples, but it’s not illegal to research specific racial-ethnic groups.

The problem isn’t legality — it’s scientific invalidity and ideological bias. NIH is supposed to be a scientific organization, not a political one. Studies based on proprietary leftist constructs and dogma like “microaggressions” and the new “anti-racist” ideology (which looks extremely racist and rests on false empirical claims) are not scientifically valid.

It’s also a problem that taxpayers are forced to fund partisan ideological research, especially ideologies like leftism that do not enjoy majority support. This is wildly inappropriate (and should be illegal). This is another argument that NIH, NSF, and other agencies could add to their quiver.

NIH, NSF, and serious universities need a smart, rigorous Clean Science framework that defines and detects all ideology. A narrow DEI focus, like DOGE had, will miss most of the bogus grants (and they indeed did miss most of them).

Just saying “DEI, DEI, DEI” isn’t good enough and has already led to some avoidable court losses. I’m not sure that NIH leadership understands the extent and nature of the ideological bias at work. We’re a year in, and we’re still funding tons of ridiculous grants that should’ve been cancelled. In so doing, we’re richly subsidizing leftist activists who cosplay as scientists and who despise the taxpayers showering them with money. It’s a ridiculous, insulting, and unjust situation and needs swift and permanent resolution.

A Clean Science framework

I’ve repeatedly mentioned the need for a framework to eliminate ideology in scientific research. Here’s part of the Clean Science framework I built. I identified four areas of bias so far:

1. Inherently ideological research, e.g. research based on proprietary and often Orwellian leftist constructs like “microaggressions” and “systemic racism”. Such research is scientifically invalid since these constructs aren’t consensual, objective descriptions of reality.

2. Rigged research. Misinformation and vaccine hesitancy are not inherently leftist constructs and can be defined in neutral, descriptive terms. They might even be worthy of research attention, but in practice this research is always rigged to advance leftist narratives.
   
   Academic “misinformation researchers” invariably treat facts, unsettled claims, and matters of opinion as “misinformation”, and they’re always beliefs associated with conservatives – leftist misinformation is completely ignored (e.g. the COVID shots prevent infection, sterilizing children (puberty blockers + cross-sex hormones) prevents suicides, community mask use (even cloth) definitely reduces COVID spread, Trump suggested that people inject or drink bleach).
   
   For example, NIH funded a misinformation study that treated statements like “Coronavirus is genetically engineered” and “Democrats are using the coronavirus situation to harm President Trump” as misinfo. In fact, this is the same researcher (Sean Young at UC Irvine) I cite in my Daily Caller article for conducting the study of the harms of one of President Trump’s optimistic tweets, and who just got another $651,586 from NIH for more “misinformation” research.

3. Suppression of research and data that conflict with leftist ideology. The principal example is denial of race as a health factor and ideological efforts to erase everything we know about race and health. This is driven by the new leftist race ideology that cannot accept race differences on any dimension (even mundane medical realities), unless they’re derogatory claims about white people. Leftists are fully willing to endanger patients by ignoring scientific evidence about race and health.

4. Missing research. There are vast domains and research questions that NIH completely ignores because of its profound leftist bias. This is likely their most impactful bias, the way in which NIH is most harmful to the American people.
   
   a. Benefits of religious faith, church attendance, prayer, etc. This was an emerging area of research before 2010, and then mostly disappeared, at least at NIH, as the left radicalized. Most such research now is foreign, with some notable work on effects of Islamic prayer (Salat), for example.
   
   b. Male suicide. American men commit suicide at extremely high rates, much higher than any other English-speaking country, especially white and American Indian men (28 and 39 per 100k, respectively), and 4x the rate for women. Surprisingly, NIH has nothing focused on this. The research community is biased against men, especially white men.
   
   c. Relatively less focus on individual behavior and health outcomes, in favor of “social determinants of health” and leftist dogma like “systemic racism” asserted as causes. There’s little account of human agency, internal locus of control, lifestyle, etc. It was inevitable that the NYT would publish a piece criticizing the RFK/Hegseth 100-50 challenge, for example, and there’s persistent tension between the MAHA premise of individual agency and the newish leftist embrace of Big Pharma/reflexive defense of arbitrary pharmaceutical products.

As you can see, a DEI-only approach will miss most of the ideological bias at NIH and NSF. It doesn’t account for large swaths of leftist ideology, including the Orwellian “misinformation” and “vaccine hesitancy” research. Nor does it account for suppressed data/research or the gaping holes in NIH’s portfolio. I think the missing research is the most impactful bias of all, particularly on male suicide and the health benefits of religious practice.

At this point, there’s a real risk that we’ll waste this precious opportunity to systematically address the way the left has captured and corroded NIH and NSF. This is our opportunity to fully remove leftist ideology from federally-funded “scientific” research and to reform these institutions to actually conduct/fund high-impact research that they’ve avoided because of their political ideology. Without a systematic approach and framework, nothing will change long-term.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and contact him at gravity at protonmail.com.

This year I worked as a senior advisor to the NIH Director, Jay Bhattacharya. My mission was to build a framework to eliminate ideological bias in NIH-funded research. The Daily Caller has now published my article about my experience.

An example of the biased research: A team of leftist academics conducted an entire study around this tweet from the President:

The idea was that people took COVID less “seriously” after the tweet. I’m sorry for the flashback to when leftists/Dems needed everyone to feel the exact level of panic and doom with COVID they felt (and share their false beliefs on the efficacy of school closures, masks, and COVID vaccines1).

NIH funded that study.

And they just gave the researcher another $651,586 for “misinformation” research in July, including $200,000 from the Office of the Director.

Unfortunately, Jay ghosted me after hiring me. He wouldn’t respond to messages or questions. In the end, it seemed like he didn’t want to eliminate these absurdly biased grants or address the problem in a systematic way – at least not now.

There must be an explanation for Jay’s behavior – I just don’t know what it is. For example, he bizarrely deleted the last couple months of my messages to him (keeping the older ones), including the synopsis of the framework. That’s inexplicable, makes no sense at all.

This was the strangest, most unprofessional experience of my career. For 4½ months, I was building the framework, reading tons of grants, sending Jay questions and examples, and it was dead air – it was like talking to a toaster.

The contractors working in the NIH Office of the Director, myself included, were laid off in late September (≈20 people). No warning or explanation. I only worked for and reported to Jay, so it was very awkward that he didn’t say a word to me when this happened.

The end result is that NIH is still funding invalid, ideologically driven “research” that has no chance to improve Americans’ health. The initial grant cancellations you might have heard about earlier this year just scratched the surface. They were based on a keyword search, probably by DOGE, that was missing lots of keywords. A systematic, rigorous framework is necessary to do the job, especially if we want lasting change, and that’s not happening. It’s what I was trying to implement, but Jay wouldn’t let me.

My framework identified four areas of bias so far:

1. Inherently ideological research, e.g. research based on proprietary and often Orwellian leftist constructs like “microaggressions” and “systemic racism”. Such research is scientifically invalid since these constructs aren’t consensual, objective descriptions of reality.

2. Rigged research. Misinformation and vaccine hesitancy are not inherently leftist constructs and can be defined in neutral, descriptive terms. They might even be worthy of research attention, but in practice this research is always rigged to advance leftist narratives.

Academic “misinformation researchers” invariably treat facts, unsettled claims, and matters of opinion as “misinformation”, and they’re always beliefs associated with conservatives – leftist misinformation is completely ignored (e.g. the COVID shots prevent infection, sterilizing children (puberty blockers + cross-sex hormones) prevents suicides, community mask use (even cloth) definitely reduces COVID spread, Trump suggested that people inject or drink bleach).

For example, NIH funded a misinformation study that treated statements like “Coronavirus is genetically engineered” and “Democrats are using the coronavirus situation to harm President Trump” as misinfo. In fact, this is the same researcher (Sean Young at UC Irvine) who I mentioned above, who conducted the study of the harms of the President’s tweet, and who just got another $651,586 from NIH for “misinformation” research.

3. Suppression of research and data that conflict with leftist ideology. The principal example is denial of race as a health factor and ideological efforts to erase everything we know about race and health. This is driven by the new leftist race ideology that cannot accept race differences on any dimension (even mundane medical realities), unless they’re derogatory claims about white people.

Leftists are fully willing to endanger patients by ignoring scientific evidence about race and health.

4. Missing research. There are vast domains and research questions that NIH completely ignores because of its profound leftist bias. This is likely their most impactful bias, the way in which NIH is most harmful to the American people.

a. Benefits of religious faith, church attendance, prayer, etc. This was an emerging area of research before 2010, and then mostly disappeared, at least at NIH, as the left radicalized. Most such research now is foreign, with some notable work on effects of Islamic prayer (Salat), for example.

b. Male suicide. American men commit suicide at extremely high rates, much higher than any other English-speaking country, especially white and American Indian men (28 and 39 per 100k, respectively), and 4x the rate for women.

Surprisingly, NIH has nothing focused on this. The research community is biased against men, especially white men.

c. Relatively less focus on individual behavior and health outcomes, in favor of “social determinants of health” and leftist dogma like “systemic racism” asserted as causes. There’s little account of human agency, internal locus of control, lifestyle, etc.

It was inevitable that the NYT would publish a piece criticizing the RFK/Hegseth 100-50 challenge, for example, and there’s persistent tension between the MAHA premise of individual agency and the newish leftist embrace of Big Pharma/reflexive defense of arbitrary pharmaceutical products.

Note that all the example grants listed are ACTIVE grants. DOGE didn’t have the right search terms to get most of the ideologically driven research, and a systematic approach – an objective framework – is necessary to root out the bias.

We need to define ideology in a consensual, rigorous way and build tools to help staff understand and detect when ideology has been embedded in research. Keywords aren’t enough.

This should happen at every funding agency, including NIH and NSF. I recommend that the administration deploy an empowered person in each agency who thoroughly understands leftist ideology and can build an objective, rigorous anti-bias framework.

The status quo is basically a lucrative subsidy program for leftist activists/academics, and much of the “research” harms conservatives in particular or Americans as a whole.

And to be clear, Jay was given all of this and more. Every active grant example here is something he was alerted to by July. He had the framework synopsis, including the above four areas of bias and much more, and deleted it (?). I have no idea why.

In the next installment, I’ll dive into the framework and grants. There’s no escaping the question of what’s ideological and what isn’t, and that question needs to be rigorously addressed in any anti-bias framework.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. Find him on X here. You can contact him at gravity at protonmail.com.

In May, Yale economist Jason Abaluck challenged me to a $75,000 bet over his ridiculous Bangladesh masking study:

“If you want me to reply further, let’s each put up $75,000. We’ll hire three judges with experience running large field experiments but no “dog in the fight” with regard to masking. We’ll pay them $10,000 each.

“They’ll read the paper, you can present your critique and I’ll present my rejoinder.

“While we’re at it, you can do another 30 min summarizing the totality of the evidence on masking and I’ll do the same.

“The judges will vote on whether the paper is generally well-done and an important contribution to the evidence on masking, as well as which of us gives a more compelling summary of the totality of the evidence. On each issue, the winner gets half the remaining $120K.

“If you accept these terms or something like them, I’m happy to discuss further. If not, please ask yourself what it means “epistemically” that I am willing to bet $75K on this for a chance of winning $45K, while you are afraid to do so.”

Hilarious. And creepy. It was too disorienting to write about at the time, so I let it sit for a few months.

If you’re unfamiliar, the Bangladesh study was a cluster-randomized trial that assigned different villages to mask propaganda conditions. The researchers handed out masks — custom designed cloth and surgical masks. They observed mask wearing and measured COVID cases.

1. Most people in the masking arm didn’t wear masks. They didn’t have assigned mask-wearing like DANMASK.

2. Their researchers/observers weren’t blind to condition, and the same observers assessed both control and mask villages... Plus the masks they handed out were very salient given their unique color and design.

3. They mostly observed OUTDOOR masking. I forget whether they entered some buildings.

4. The difference between the two groups is 20 cases of seroprevalence in a sample of over 300,000 people. Twenty. We can’t possibly trust research based on 20 cases in such an enormous sample. They did not disclose this fact in the paper.

5. The Ns are wrong or the descriptions of the Ns are wrong.

6. It was in Bangladesh, a vastly different culture with a much higher population density than the US, limiting even a valid study’s application to the US and other Western countries.

Using complex inferential statistics that I haven’t examined and which we shouldn’t trust, they found a meaningless effect for surgical masks, but only for the elderly. They found no significant effect for cloth masks, but non-effects are as meaningless as effects since the study was invalid. (I recommend not trusting inferential statistics in academic research in general, especially anything complex that rides lots of researcher decisions, that you can’t validate yourself, and where descriptive statistics are not disclosed. Especially research that advances leftist narratives – it’s almost certainly false and/or invalid.)

See my full treatment of the scientific evidence on masking and COVID, and common fallacies on the topic, here.

Abaluck told the media: “I think this should basically end any scientific debate about whether masks can be effective in combating covid at the population level.”

THAT is what drew my attention to the study. Scientists don’t talk like that. Especially when you’re talking about a junk study that isn’t even a proper randomized trial, with all the problems noted above, where your effects were so faint and mushy, and where the topic is something as potentially complex as mask efficacy in the real world. It was incredible to say it ends the scientific debate! A Bangladesh study that didn’t randomly assign or control masking behavior is a terrible basis to make such sweeping claims, and they didn’t even find an effect for cloth masks anyway.

Back to the bet. It’s a bet on the opinions of academics (!), specifically whether these academics would rebuke a famous, peer-reviewed study published by a tenured professor at Yale, one of their own...

Not only that, a study that aligns with leftist dogma on masking (albeit newly-acquired dogma — leftists add arbitrary empirical beliefs to their long list of mandatory beliefs at a steady clip).

God no, this is absurd. Leftist academics, i.e. academics, won’t even call out scientific fraud if it’s aligned with leftist narratives. I know this from painful experience. You can publish a table of identical, false factor loadings — which is unprecedented and insane — as Lewandowsky, Oberauer, and Gignac (2013) did, and no one is willing to call it out because his fraudulent study maligns conservatives.

In any case, the bet already happened. Abaluck, et al.’s invalid study was peer-reviewed and published by Science, a once prestigious journal now notorious for publishing fraudulent COVID origin papers, false papers on other topics to advance leftist narratives, and for having a deranged partisan maniac as editor, my former Chancellor at UNC, Holden Thorp.

The fact that this junk was published argues against betting on members of the same cloistered, corrupt academic culture even noticing its incredible flaws. Of course, the bet would have me present those flaws, but then I’d be relying on the integrity and intelligence of academics, and I wouldn’t even bet $100 on that. No way, especially not on the criteria: “is generally well-done and an important contribution to the evidence on masking”. That’s mushy academic blather, especially the “important contribution” bit – they’ll be loathe to say that it’s NOT an important contribution.

It’s not just that Abaluck could be in a position to review their own papers and grants (anonymously) and those of their students and friends — it’s that academia is a haven for cowards who all subscribe to the same authoritarian leftist ideology. They don’t call each other’s research invalid and not important, especially if leftist politics is activated, as it is with masking and COVID. Academia is structured in the exact opposite way that you’d design a truthseeking institution.

Finally, while I generally dislike the use of the word “bullying” to refer to interactions between adults (as opposed to children), I think an overpaid1 academic challenging someone to put up $75,000 to continue to discuss his incredibly invalid research probably qualifies as bullying, or at least incredibly elitist and rude. How many people can bet $75,000 on anything? Not many. You pretty much have to be rich (and wifeless) to be able to risk $75,000 on such a risky bet — that leftist academics will rebuke the validity and quality of an authoritarian leftist-aligned paper. You have to be willing to just burn $75k. I don’t think most people even have $75k that they can spend on anything without withdrawing from a 401k, with penalties.

You can see the full thread by clicking on the post below:

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can find him on X here and email him at gravity at protonmail.com.

p.s. Abaluck’s first reply was “Obviously I think you are misinformed or lying.” Who lies about their own views? What is it with leftists always accusing people of lying when they express disagreement with a leftist belief or position? I don’t understand the theory of mind here. They really think a person who writes a critique of their study is lying? Lying in their critique? How would that work?

p.p.s. I don’t know that Abaluck is a leftist – he’s an authoritarian in the way lots of economists are. One gets the impression that many economists would turn you into dog food if it increased “general welfare” or reduced “inequality” (variance in incomes or wealth) according to their equations. (”Equality” is a completely arbitrary, leftist value that they often embed without explanation. Notably, they don’t embed liberty or autonomy as obvious values to optimize – only leftist values.)

From Carlisle we have the following melancholy Intelligence, viz… That on the Twenty-sixth of July, in the Forenoon, the Enemy came upon a School-house, on Conecocheague Creek, about Twelve or Fifteen Miles from Fort Loudoun, in the very Heart of the Settlement, where they killed and scalped the Master, one Brown, and Nine of his Scholars : That there were four more belonging to the School, who, it is thought, were made Prisoners, as they had not been heard of : That this bloody Massacre was first discovered by a Man passing by, who, hearing no Noise in the School, went in, and saw the Master lying scalped…

Such was America’s first known slaughter of school children, reported by the Pennsylvania Gazette, Ben Franklin’s newspaper, on August 9, 1764, two weeks later. (See a scan of the original paper in the Appendix.) Indian “warriors” clubbed and scalped ten students after shooting the schoolmaster, Enoch Brown. One scalped boy — Archie McCullough — survived. More here.

For most of human history, people didn’t know about things that happened more than 200 miles away. They were exposed to the behavior of only a few hundred other people, maybe a couple thousand.

In 1764, it took two weeks for the people of Philadelphia to learn of a school massacre ≈170 miles away. (Travel time was 5-8 days.)

One hundred and sixty-three years later, on May 18, 1927, the Bath School in Michigan endured what is still the worst school slaughter in American history. Thirty-eight children and six adults were murdered in two carefully planned dynamite explosions.

An example of major American newspaper coverage: the Los Angeles Times reported the massacre the next day, ran a page-four follow-up on the 20th, and never spoke of it again. Newspapers in smaller cities didn’t report it at all. President Coolidge didn’t mention the massacre, and I don’t think it occurred to reporters to ask him about it. Charles Lindbergh took off on his historic flight to Paris on May 20 and America seemed focused on that.

Things have changed. America is now a continent-spanning nation of 340 million. Because of mass and social media, we instantly learn of every horror that any of those 340 million people commit, even if it’s 2,000 miles away.

We don’t just read about it — we’re marinated in it, our horror stoked, shaped, and amplified by TV anchors and carefully curated interviews of officials, bystanders, and politicians.

We’re marinated for about a week, but that’s not the end of it. Legacy media and politicians will re-run this emotional manipulation on the anniversary of the shooting. And the second anniversary. And the fifth.

This is an unprecedented, unhealthy, and extremely unconscious way for humans to live. These campaigns also terrify people with false statistics for mass school shootings, and they’re premised on a basic error: That America is a town, as opposed to a nation of 340 million.

That premise is implicit in statements like “children deserve to feel safe in their schools”, “children shouldn’t be afraid to go to school”, and “it keeps happening!”. It would make sense to talk this way if these horrors kept happening in one’s town, city, or region — it makes no sense to talk this way if you live in a vast nation of over 300 million people that averages less than one mass school shooting per year.

Children are safe in their schools with respect to mass shootings. America has roughly 100,000 K-12 schools and there have been a total of ten mass school shootings this century / the last 25 years (using the classic federal definition of 4+ victims killed).

If you thought there were more, hold that thought. I’ll explain where the inflated estimates come from and I’ll cover lower thresholds — I won’t evade any realities here. In any given year, the odds of a school having a mass shooting are about 1 in 100,000, since we’ve seen one incident per year for the last four years, though zero in most years this century.

There are roughly 50 million school-age children in the US. Given current trends, we might lose four of them to a mass school shooting in any given year.

Given those facts, it’s deeply confused and innumerate to suggest that children aren’t safe in schools. The last thing anyone should be worried about is mass school shootings. Of all the threats to America’s children, mass school shootings are not one of them.

I knew something was very wrong when, after the Uvalde massacre, the woman I once saw as the love of my life posted on Facebook that she’d dressed her son for school and wasn’t sure if she’d ever see him again…

A horror perpetrated by a 1-in-80 million monster over a thousand miles away made her think there was a real chance her son could be slaughtered. This was incredibly sad and disturbing, and captured a dysfunctional reaction that seems to be widespread.

Here’s what’s happening:

Many Americans are psychologically grouping an incident that happened 1,200 miles away, another incident two years later and 800 miles in the opposite direction, and an incident the year after that and three states away as something that “keeps happening!”, with no apparent awareness that they live in a nation of 340 million people and that they wouldn’t even know about these horrors if they lived in any other era.

They’re treating America as a town or a small region, collapsing all these continent-spanning horrors into a singular cohesive and threatening phenomenon.

Humans can’t comprehend numbers like 340 million and they’re not thinking about America’s enormous size when they hear about a slaughter a thousand miles away.

They’re also not conscious of the effects of mass and social media, that we’re the first humans to be instantly blasted with news of such distant horrors, and that we should probably take steps to better manage this. It’s extremely unhealthy to be plugged into the behavior of 340 million people without some mitigations and a fuller cognizance of the implications.

Critically, they’re missing the nature of the act. Walking into a school, pointing a gun at children, and pulling the trigger is impossible for virtually all humans. It’s impossible. It’s impossible for you, me, and even hardened violent criminals. Many of the convicted murderers on death row couldn’t do it. Your body wouldn’t work – your legs, shoulders, arms, hands would not comply and perform the necessary movements.

Why is this important? Because this isn’t “gun violence”. This is profoundly different, a darkness beyond our reckoning. We don’t know what this is, what makes these monsters. Thankfully, they’re extremely rare. There are over 80 million people aged 14-32 in the US, the age range of the ten mass school shooters this century. In any given year, one of those 80+ million people might commit a mass school shooting. We won’t stop these monsters by banning the future retail sale of a type of rifle — that’s what an “assault weapons” ban means. They’d just use a different firearm or an existing rifle (several shooters have used someone else’s rifle, typically a parent’s).

Finally, people are deceived by false statistics from gun control lobbies, politicians, and left-leaning media. Those statistics obscure the extreme rarity of the act, and thus its nature, by making people think there are hundreds of mass shootings a year.

False stats

You’ve probably seen much higher numbers for mass school shootings or just “school shootings” than the ten I claimed for this century / the last 25 years.

The total of ten is based on the standard definition of 4+ victims killed. It’s a good definition because it gets at the canonical case of a monster walking into a school and firing away at classrooms full of students.

What if we lower the threshold to 3+ victims killed? Using Everytown’s data going back to 2013, this adds only one incident. There were seven mass school shootings (4+) in that span, so dropping to 3+ results in eight.

If we drop to 2+ victims killed, we get nine more incidents, for a total of 17 since 2013 (including the recent Minnesota Annunciation Catholic School shooting). Two caveats here — I’m talking about:

1. Shootings at a K-12 school.

2. That killed students of that school, which I think we assume when we think of a “mass school shooting”.

These caveats filter out random gang violence like a shootout at 1:30 am in the parking lot of an elementary school, an incident that Everytown includes in its database as a “school shooting”.

So where do the higher numbers come from? From anti-self-defense groups like Everytown, Giffords, and the Gun Violence Archive (GVA). For example, as of September 30, 2025, Everytown claims that there have been 109 “incidents of gunfire on school grounds”.

How do they arrive at such numbers? Well first, note that they’re referring to “gunfire”, not mass school shootings, though their numbers will end up in people’s heads as “mass shootings” after they’re laundered through legacy media.

Everytown counts a school resource officer (security guard) accidentally discharging his weapon in his office, not shooting anyone, as a shooting.

Or when a bullet fired in a nearby carjacking hits a school window, that’s a school shooting.

Second, they include colleges and universities to pad the numbers. Even so, the incidents are not what people would assume. Examples:

In February 2025, a police academy instructor at Daytona State College accidentally shot himself. Yes, Everytown is counting a police academy accident as a school shooting.

In June 2025, two Texas Tech police officers investigated a suspicious vehicle in the stadium parking lot at 1:00 am. The driver fired on them, the police returned fire, killing the criminal and suffering gunshot wounds themselves. “School shooting”?

In July 2025, there was a shooting at an elementary school in Akron, Ohio.

At 3:00 in the morning…

It was gang activity in the parking lot. School shooting?

In fact, the fastest way to code an incident as not a “school shooting” is to find out what time it occurred. If it was at oh-dark-thirty, it’s not a school shooting.

Every time an actual school shooting happens, legacy/leftist media will present false statistics from anti-self-defense groups, driven by unrelated incidents like those above, making people think these horrors are far more common than they are.

I said there have been ten mass school shootings this century. I wasn’t lying. There have been ten. Since 2013, there have been seven. If we drop to 3+ victims killed, we have eight. If we drop to 2+ victims, we have 17 since 2013, some of which looks like gang violence. (I don’t have granular data for pre-2013.)

Every time it happens, leftists lament that “It keeps happening!”. Yes, this is the nature of time. Things that happen once a year in a nation of 340 million people will likely keep happening once a year. Everything that happens keeps happening. Murder, rape, kidnapping, overdoses, cancer. It’s not new information when a mass school shooting happens — we already knew about them. Yes, they’ve increased — from zero per year to one per year. Can we prevent them? Possibly, but it’s much easier to reduce the overall murder rate.

Charlie

Let’s zoom out to the broader issue of gun violence and disarmament. After a leftist assassinated Charlie Kirk, other leftists smeared him by deceptively quoting from his huge corpus of conversations and debates, in some cases even fabricating quotes outright — e.g. the fake “Black women…” quote fabricated by Washington Post columnist Karen Attiah and others.

One micro-quote is perhaps the most circulated:

I think it’s worth it to have a cost of, unfortunately, some gun deaths every single year so that we can have the Second Amendment to protect our other God-given rights.

This came from an April 2023 TPUSA event and is often presented as somehow ironic given that he was murdered with a firearm. Leftists have also falsely claimed that Charlie “condoned” gun violence — i.e. murder — even that he “condoned” school shootings. A viral formulation by leftists on social media is: “I don’t condone what happened to Charlie Kirk, but Charlie Kirk condoned what happened to Charlie Kirk.” This is as false as a string of words can be, and it highlights how deranged and confused the modern left is.

This is a profound cognitive collapse, a lack of theory of mind, and an inability to understand principles or even words. Before we go further, here’s the full quote from Charlie (link):

You just don’t want to face reality that governments tend to get tyrannical and that if people need an ability to protect themselves and their communities and their families.

Now, we must also be real. We must be honest with the population.

Having an armed citizenry comes with a price, and that is part of liberty.

Driving comes with a price. 50,000, 50,000, 50,000 people die on the road every year. That’s a price. You get rid of driving, you’d have 50,000 less auto fatalities.

But we have decided that the benefit of driving — speed, accessibility, mobility, having products, services — is worth the cost of 50,000 people dying on the road.

So we need to be very clear that you’re not going to get gun deaths to zero. It will not happen.

You could significantly reduce them through having more fathers in the home, by having more armed guards in front of schools.

We should have a honest and clear reductionist view of gun violence, but we should not have a utopian one.

You will never live in a society when you have an armed citizenry and you won’t have a single gun death. That is nonsense. It’s drivel. But I am, I, I — I think it’s worth it. I think it’s worth it to have a cost of, unfortunately, some gun deaths every single year so that we can have the Second Amendment to protect our other God-given rights.

That is a prudent deal. It is rational. Nobody talks like this. They live in a complete alternate universe.

So then, how do you reduce? Very simple. People say, oh, Charlie, how do you stop school shootings? I don’t know. How did we stop shootings at baseball games? Because we have armed guards outside of baseball games. That’s why. How did we stop all the shootings at airports? We have armed guards outside of airports. How do we stop all the shootings at banks? We have armed guards outside of banks.

How did we stop all the shootings at gun shows? Notice there’s not a lot of mass shootings at gun shows, there’s all these guns. Because everyone’s armed. If our money and our sporting events and our airplanes have armed guards, why don’t our children?

As you can see, the micro-quote doesn’t convey his point very well. Charlie was saying that some level of murder is inevitable in a large population, and if the population is armed, some of those murders will be with firearms. He further says that the Second Amendment helps secure other God-given rights, and that having some gun deaths is worth securing those other rights (presumably freedom of speech, religion, due process, etc.).

He did not condone any sort of violence, whether gun violence or otherwise. In fact, he spoke of how to reduce school shootings. As a Christian, he obviously would never condone violence (much less murder) but American leftists don’t understand who Christians are or their ethical framework.

Charlie revisited the topic at length in a December 2024 campus conversation — the very type of event at which he was murdered nine months later.

A student presented the following argument for disarming the public:

So the United Kingdom, there was the Dunblane massacre in March 13th 1996. It was a school shooting. 17 people were killed… Following that massacre… there was a ban on all handguns. Since then, there’s been no school shootings in the UK, and it averages 28 firearm homicides a year. In the United States, there are 39,707 gun deaths in 2019 alone, the average of one gun death every 11 minutes. In 2023, there were 346 school shooting incidents in the US. So far there have 385 mass shootings in 2024…

Let’s walk through the claims.

Since the ban, there have been no school shootings in the UK.

There were no mass school shootings in Britain before Dunblane, so it’s not meaningful that there were none after. He implied that banning handguns ended something – it ended nothing.

In fact, there were no mass school shootings in the United States before 1989, in data going back to 1966. This is a new horror, albeit a rare one.

The UK averages 28 firearms homicides a year.

False, and who cares? I don’t care how many firearms homicides there are in any country. This is the Firearms Fallacy. He mistakenly focused on firearms homicides when what we care about are all homicides. It’s tautological to reduce firearms homicides by banning firearms, and it doesn’t tell you if you’ve accomplished anything. Why? Because maybe the overall homicide rate stayed the same or even increased, due to substitution or other factors. Here’s their homicide rate:

As you can see, the homicide rate didn’t fall after the 1997 handgun ban – it increased! And it was lower historically, and now it’s back to early 1980s levels. It doesn’t look like banning handguns did anything, at least not by the leftist here’s-a-number or here’s-a-graph method, where there’s no actual analysis, no modeling or controls, no attempt to get at causality. (For whatever reason the British Office of National Statistics data focuses on England and Wales, excluding Scotland and Northern Ireland. This should be fine since England and Wales comprise about 90% of the population.) Here’s their suicide rate:

Not much happening there, and the 2023 levels are same as 1997. Now, the UK does have a much lower homicide rate than the US — I’ll address that further down.

The US had 39,707 “gun deaths” in 2019.

So what? How many are there supposed to be? Beware of raw numbers in the context of large populations. 340 million is an enormous population and 2,854,838 people died in the US in 2019, so about 1% via firearm.

Most of those 39,707 “gun deaths” were suicides. Do we care how people commit suicide? I don’t, unless you give me a reason to. And do I think we should be reaching out into the world to prevent every suicide? No, I do not. I can’t judge or evaluate suicides from a distance, and I will not mix suicides and murder for gun control discussions — they’re profoundly different things.

The leftist arguing with Charlie threw in suicide deaths to spike the numbers in a conversation about crime, mass school shootings, etc. This is typical of the mindless leftist approach to this topic – just toss out random numbers, as large as possible, unintegrated, unsystematic, no cohesion, no analysis.

One “gun death” every 11 minutes

Okay… How many minutes is it supposed to be?

This is a meaningless factoid, just repeating the “N gun deaths per year” — which is mostly suicides — by dividing N into the number of minutes in a year. They’d presumably react the same if the number was 17, 20, or 9 minutes (it’s actually every 13 minutes, not 11, given the 39,707 figure). Have you ever thought about how many minutes should pass between any type of tragedy? Taking this every N minutes approach is especially barbaric in a nation of over 300 million people. It’s another example of a statistic that is poorly suited for large populations, and leverages people’s ignorance of the population size.

346 school shooting incidents in 2023

This is wildly false. He’s using statistics from the Gun Violence Archive (GVA), a leftist website that lies about “gun violence” to generate inflated and alarming statistics. They tally “school incidents”, the large majority of which did not involve any shooting, so it was false to refer to them as “shooting incidents”. Most of them are a gun found in a backpack, often unloaded.

Even when gunfire is involved, it’s often not at a school. GVA describes one of their incidents thusly: “Gun fell and discharged from camper and hit school bus transporting students.” No one was harmed. This happened on a road in Wasilla, Alaska. It was not any kind of school shooting.

Another incident: “man fired handgun in the air while children were waiting on school bus at bus stop”. No further details are given. It wasn’t at a school.

Even when someone is shot, it’s often not at school. A 15-year-old boy was shot by rival gang members in Everett, Washington, not at school. The GVA asserts that this happened at a school bus stop, even though that’s not mentioned at all in the news article they site.

385 mass shootings in 2024

He’s using a definition that doesn’t require anyone to be killed, so this statistic is mostly gang violence, as Charlie notes in the video. There were only four mass shootings in 2024 using the 4+ victims killed definition (one school shooting and three non-school).

I don’t care about any of these numbers – I care about the murder rate. That’s the bottom line, not shootings, “gun violence”/suicides, mass shootings, how many minutes pass between suicides, the ratio of bullet to cupcake sales in the US, etc. The bottom line is murder, and mass shootings account for fewer than 1% of murders.

Let’s step back and address the big picture here, the British comparison, and solutions to violent crime, including gun violence.

Solutions

We can significantly reduce the murder rate. Reducing mass shootings will be harder because of their extreme rarity.

Leftists/Democrats talk about “gun violence” as though Americans are broadly guilty of it. This is very awkward and suggests that they don’t have an accurate theory of mind for humans, or at least Americans. Gun violence consists of murder and attempted murder. That’s extremely dark, unethical behavior that normal people don’t engage in. It’s on the order of a 1-in-2,000 behavior. For most of us, it’s simply not possible, and by most, I mean over 99.9%.

This failure of theory of mind is most salient when leftists cite junk studies claiming that if “you” own a firearm, “you” are more likely to escalate domestic violence to shooting your partner or spouse. Who are they talking to?? Most Americans have never engaged in domestic violence — there’s nothing to escalate. (About a third of Americans report any history of DV, either being violent or a victim of it.) Moreover, only a tiny percentage of people who are violent at home will ever try to murder their partner or spouse — most of them are not capable of such acts. It’s incredibly dark and delusional to think that most humans are capable of murdering their loved ones, and that a hunk of steel in a drawer will whisper to them like the One Ring.

No, most humans are extremely reliable at not being murderers or violent criminals. Disarming those people accomplishes nothing and empowers predators. Criminals are a special population, and a tiny share of the population is responsible for most crimes. The best way to reduce crime is to arrest and imprison criminals, as we’ve seen in El Salvador, DC, and any jurisdiction that takes crime seriously. It’s simple physics: Identify criminal → Remove criminal from society = No more crime from that criminal.

However, Democrats oppose this. In fact, they exploded the murder rate in 2020 with their nationwide anti-policing and anti-prosecution movement, marked by riots, arson, shootings, and the election of extremist prosecutors dedicated to not sending violent criminals to prison.

This campaign cost many thousands of lives every year from 2020 through 2023 as the murder rate exploded by 30%. This phenomenon is known as the Ferguson Effect, named after the crime-increasing effects of the leftist riots driven by false claims about the 2014 shooting of Michael Brown in Ferguson, Missouri.

So while Democrats scream about mass shootings that comprise fewer than 1% of all murders, and under a hundred victims per year, they deliberately undermined policing and prosecution nationwide such that more than 10,000 additional people were murdered from 2020-2023. (As you can see, 10,000 is a very conservative estimate — about half of the increase in murders — to allow for other factors.)

Thus it’s difficult to take their professed concerns about gun violence seriously — they’re quite willing to increase such violence in spasms of saccharine rage over false claims of racism in policing. And they support releasing violent criminals an infinite numbers of times. The difference between mass school shootings and the murder of Iryna Zarutska, for example, is that we can easily prevent the latter. There is no reason on earth why a man who has been arrested 14 times should be free to prey on the innocent. This scene should not be possible.

It shouldn’t even be possible to be arrested 14 times because the third or fourth arrest should be the last, securing permanent lodging in a state institution.

That Democrats refuse to lock up predators and protect the innocent is a key driver of America’s murder rate. Their rhetoric around mass shootings is a distraction from the enormous number of murder victims their policies have created.

As far as once-a-year mass school shootings in such an enormous nation, I’m not sure how to prevent them except by guarding schools, as Charlie recommended. However, it’s hard to guarantee the necessary level of competence and compliance across ≈100,000 K-12 schools, and having armed security will lead to a few accidents a year (which will be exploited by leftist groups and mislabeled as “school shootings”) and probably a rape or five per year. I mean that if you send 100,000 – 400,000 additional men into schools, a small number of them will rape someone at the school. You can hardly do anything on a nationwide scale — given a nation of this size — without killing someone or getting someone raped. I was reminded of this when Democrats wanted to ban vented dryers because of a few fires a year. This would require millions of visits by men installing the new, weaker dryers – congratulations, you just got more women raped. The bottom line is that it’s hard to prevent very rare events on a national scale without triggering harms that approach those that were prevented.

Britain

What about the British comparison? It’s true that they have a much lower murder rate than we do, approximately 1 per 100,000 people compared to ≈5.5 in the US in recent years (it climbed to 7 per 100k during the Dem-induced murder explosion).

However, states like Maine, Rhode Island, Iowa, New Hampshire, and Hawaii have murder rates close to Britain’s, at 2.0 or less most years. Murder in the US is heavily driven by minorities, especially blacks, who commit a majority of murders most years even though they’re only 13% of the population.

Addressing black culture is as important as ending Democratic catch-and-release policies. For too long, an obviously dysfunctional, irresponsible, and anti-flourishing culture has been valorized, fetishized, and insulated from criticism. Any culture where nearly 70% of babies are born out of wedlock should be treated as an emergency. You simply cannot have good outcomes with nearly 70% of babies born out of wedlock — that’s a complete destruction of the family, with downstream impacts on criminality, education, and income.

What about gun control / disarming the public, like Britain does? If you could snap your fingers and make all the guns in America disappear, sure, that would eliminate gun murders and likely reduce the overall murder rate for a few years. Two things:

1. We don’t have such magic powers, and there is no possibility of legally disarming the American people. Any attempt to ban firearms ownership would lead to most states seceding, possibly even civil war — which the free states would win — so there’s no point even thinking about it. Just forget it.

2. It would be extremely harmful to magically or legally disarm everyone and would be a savage violation of the basic human right of self-defense. English (2021) estimates that Americans use firearms defensively 1.7 million times per year. Firearms are excellent at stopping crimes, usually without firing them.
   
   A drop in the murder rate from a magical gun ban would be asymmetric, mostly sparing gang-bangers — lots of innocent people would be murdered because they had been disarmed and rendered defenseless against predators. It would also enable Oct 7 type horrors in America, from either border (it’s underexposed that the Israeli civilians were generally unarmed, even at home — the Israeli government mandates that its people are defenseless, except for highly regulated handguns).

With smart, focused leadership, the US could reduce the murder rate to 4 per 100k, close to 1950s levels. President Nayib Bukele in El Salvador reduced it to 1.9, so perhaps we should be more ambitious, but El Salvador is a much smaller country with a very different context.

Note also that the Britain is now a Orwellian tyranny whose government arrests a thousand people every month for their social media posts and recently sent five armed police to arrest comic writer Graham Linehan as he got off the plane in London. Why? Because he said that if men who assert that they’re women invade women’s spaces and refuse to leave, the women should call the police and if that fails, punch them in the balls. He also called radical trans activists misogynists and homophobes, and said that he hates them. That’s it.

The British government bans knives and even pepper spray — there is no right to life or self-defense in Britain — and is now talking about banning pointed kitchen knives… Yes, they have a low murder rate, but I think they’d have a low murder rate even if they were armed. The British people have a very small sense of themselves and their standing in relation to the state, and they tolerate police coming to their homes to arrest them for a tweet. They even tolerate Muslim immigrants engaging in the massive organized rape of native British girls. The fact that they have no right to self-defense and the corollary right to bear arms has surely helped shape and shrink their sense of themselves and drives their passivity in the face of censorship and subjugation. This video of a judge sentencing a man to prison for his social media posts speaks volumes:

This is what Charlie was talking about when he said that it’s worth it to have some gun violence in exchange for a Second Amendment that helps secure our other freedoms. I’d go further and say that humans have a right to life, which necessitates a right to self-defense, which necessitates a right to bear arms against predators, whether a thug in a hoodie or a tyrannical regime. It doesn’t matter whether these rights are written down in any particular country’s constitution — every human being has a right to life and no one has the right to take it away.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

Appendix

Here’s the 1764 Pennsylvania Gazette article on the Enoch Brown school massacre. Note that their orthography was a bit different — the character that looks like an f without a crossbar is a lower-case s.

Data is overrated. This is a terrible, terrible thing for a scientist to say. Well, except that it’s true, so it’s a good thing for a scientist to say.

I don’t question, remotely, the central empirical premise of social science or any other science. Social science requires the rigorous collection of data and always will. This is good and fine, and often thrilling and wonderful. I mean something different.

I think there's a problematic epistemology, particularly in social psychology, that doesn’t distinguish between the kinds of claims that require data and those that don’t. There’s a broad lack of awareness of the epistemic authority of logic – that we can know some very important things through valid reasoning. This has consequences for the validity of research, and what happens after invalid research is published. We've somehow combined rationalism and empiricism, two sides of a classic dichotomy in philosophy. We're rationalistic in our empiricism. Let me explain.

Category 1

Example 1

In 2011, I pointed out that Napier and Jost (2008) is invalid. In that paper, they wanted to explain why conservatives are happier than liberals, and asserted that it was because conservatives “rationalize inequality." Strangely, they never measured rationalization of inequality. I don’t mean that they used a measure that I don’t like – I mean they never measured it at all (and they didn’t collect any data, just used public datasets like the National Election Survey and relabeled the variables.) In one study, they took answers to the question of whether hard work tends to pay off in the long run – just that one item – and called it “rationalization of inequality”. In another study, they took a 6-item measure of simple attitudes toward inequality, and called it, again, “rationalization of inequality”. They converted an attitudes measure into a measure of rationalization of those same attitudes – I’ve read a lot of papers, but I’ve never seen a social psychologist do that.

The studies are invalid. They didn’t measure the construct. Rationalization is a cognitive process, one that social psychologists are quite familiar with, dating to classic research on cognitive dissonance. We can’t just assert that people are rationalizing something. We can’t ask people whether they agree with our political views and call it rationalization if they don’t.

I argued that the paper was completely invalid, that it carries no knowledge pertinent to its claims or hypotheses. (The authors or journal should retract it.) I received lots of supportive e-mails from researchers all over the world, but I was surprised when a couple of them said that my debunking would be even more convincing “with data.” Hold that thought…

Example 2

I recently wrote about the Lewandowsky, Oberauer, and Gignac (2013) scam (after lots of other people did.) In their title, they linked moon-landing hoaxism with climate science hoaxism, even gave it a causal direction. There was no such relationship. One reason is that virtually no one in the study endorsed the moon-landing hoax – only 10 out of 1145 participants, the lowest level of endorsement anyone has ever reported, to my knowledge. We can’t link moon hoaxism to anything if only 10 of 1145 endorse it (0.8%). Even worse, 7 of those 10 rejected the climate hoax idea, so going with the causal direction in the title, if one believes the moon landing was a hoax, one is unlikely to believe that climate science is a hoax. And if we run a logistic regression – the proper analysis for wildly skewed and substantively dichotomous data, not a Pearson correlation – with moon hoaxism predicting climate hoaxism, there is no effect. However, I don't think any analysis is appropriate when only 10 people endorse the moon hoax – 0.8% is less than the sampling error for the survey, so it's essentially zero. In any case, if we lived in a universe where we could make inferences from 10 people in a sample of 1145, and the titles of our papers were based on 0.8% of our participants, the title here would have to be the opposite, e.g. NASA Faked the Moon Landing, Therefore (Climate) Science is True.

(Ethics Pause: Think about how you would relate to someone if you thought they believed the moon landing was a hoax. Or if they disputed that HIV causes AIDS? (11 people in their sample, from which they asserted other effects...) Would you hire them? Would you date them? What Lewandowsky, Oberauer, and Gignac is a disgrace.)

They made similarly false claims in the abstract and body of the paper, linking people to beliefs that virtually no one in their sample endorsed – numbers far too trivial (11 or 16 people out of the 1145) to be linked to the mainstream views to which they tried to link them. It was a scam. They abused linear correlation statistics driven by variance between people choosing Strongly Agree vs Agree, and told the world that people “rejected” claims that they in fact agreed with.

Once more, a scientist who agreed with me also said that my debunking would be even more convincing “with data.”

What This Is

This is recurrent, and there are many examples of this mindset. It’s a profound epistemological and scientific error.

The examples above are cases where the research is invalid. In the Lewandowsky case, we might just say their claims are false, since they actually measured the variables they talked about, so we can look and see that the claims are false. (There we’d probably say that their analyses were invalid and some of their claims false and unconscionable.)

When people say that “data” would help make a case against these papers, it tells me that they don’t know what I mean when I say that a study is invalid, and I think I mean validity in the normal sense.

I think it comes to a couple of problems in how we as a field understand knowledge as such.

First, a fundamentally invalid study does not represent knowledge. It carries no findings. To say that a study is invalid normally means that the study's method is not a legitimate method of knowing about the things the study wanted to know about. Social psychologists are granting residual epistemic standing to published work, even after it's been shown to be invalid, even if they agree that it's invalid. This might be a crude social proof thing – people might think that if a paper is published it must have some legitimacy or validity. This belief will be false across all scientific fields, and I think it's a very vulnerable assumption to have about social science papers in the current era.

But there’s something else here. Say we have papers like the ones above. Then someone comes along and argues that they’re invalid, or even false by their own data. What sort of knowledge claims are these?

They're claims about the validity of research, or about the data behind claims the authors made. These kinds of claims do not require new data. Let’s linger on this...

Let’s say I measure sunshine and call it self-esteem.

You observe that I measured sunshine and called it self-esteem. You then say, hopefully very stridently, that my study is invalid, as are any claims I made.

What determines whether your argument is true? Whether I indeed measured sunshine and called it self-esteem. That’s what will determine the truth of it.

How would (new) data help your argument? Remember the claim is that I measured sunshine and called it self-esteem. What new data could you collect to further establish that I did so?

There is none. Either I measured sunshine and called it self-esteem, or I did not. If I did, my study is invalid. It doesn’t matter if I got it published in Psychological Science. It’s still invalid. It doesn’t matter if other people cited it, if it helped get me tenure, or if Chris Mooney touted my findings. None of that matters. My study is fundamentally invalid, which makes it meaningless. It does not represent knowledge about self-esteem, does not carry data about the construct. It’s vacated, as a matter of scientific knowledge, regardless of whether I have the integrity to retract it.

Is this clear? The nature of a validity argument is that it rests on what happened in the study – it’s about features of that study. It’s not about the empirical hypotheses. New data is not necessary and often won’t even strengthen the argument. There are exceptions, but not the examples here. The burden is always and entirely on the researchers to support their claims – it's never on us to go out and collect data to refute an invalid study.

In the Lewandowsky case, what would we refute with data? The claims are false by their own data. That's the claim being made. When we say “10 of 1145 participants were moon hoaxists, and most were not climate hoaxists”, that’s a claim grounded in observations about their data. We don’t need any data of our own to observe their lack of data.

We’re granting an inappropriate privilege to papers and findings just because they’re published. I think some researchers haven’t fully wrapped their heads around the fact that a paper, or a set of studies, can simply be false, or invalid, or both. We need to have those categories, because they describe recurrent phenomena in any science. We don’t have a ready schema for "false paper" or "invalid paper", outside of traditional fraud cases. In the humanities, fields without data in the normal sense, the worst that can happen to a paper (absent fraud) is that it be forgotten, or superseded. There’s no “false” category. The only way to get retracted is plagiarism or fraud in one’s sources. In science, things can be false. Things can be invalid. Papers are retracted all the time, often by their own authors, for non-fraud reasons – for having made a mistake, being false or invalid. But not in research psychology. I never see people retract their papers when someone points out that they’re false or invalid. That’s not a sustainable situation. We can’t be like the humanities in that respect – we need to be like the sciences.

I’d like to zoom back a bit and address another branch of this issue of rationalistic empiricism, and the lack of awareness of the authority of valid reasoning. The two examples here will continue to serve us. In both cases, the research was doomed before it began.

Category 2

Sometimes social scientists act as though we can’t know anything unless it’s published in one of our journals, comes with a p-value, etc. Now, I don’t think anyone would literally argue this if pressed – it’s just that they implicitly embrace something like this view much of the time. In reality, there is lots of valid knowledge outside of social psychology – the world abounds with knowledge, and sometimes knowledge and data outside our fields will have logical implications for our hypotheses.

For the N&J study, someone might have observed that since conservatives don’t necessarily object to economic inequality (neither do libertarians), they probably can't rationalize it. To rationalize something, it has to be objectionable, and we can't assume that income inequality is objectionable, either in the mind of a participant or as a descriptive fact about the world (the researchers seemed to assume that it was obviously objectionable.)

Here's a definition of rationalization from Dictionary.com: to ascribe (one's acts, opinions, etc.) to causes that superficially seem reasonable and valid but that actually are unrelated to the true, possibly unconscious and often less creditable or agreeable causes.

We definitely can't assume conservatives are doing that on income inequality – that would be biased and question-begging. So it’s not clear that the construct “rationalization of inequality” has any valid application to conservatives or other non-liberals, just as the "rationalization of abortion" would not be valid applied to pro-choice liberals and libertarians. It's a biased, question-begging scam to define "rationalization" around the researcher's problem with a social reality. I’m sure you could induce defensiveness in conservatives by showing them pictures of homeless people or something, but you could induce defensiveness in any camp, on any issue. I’m not sure that it would interesting or meaningful.

(Note that their hypothesis on why conservatives are happier than liberals was refuted later by Schlenker, Chambers, and Le (2012). Their paper was not debunked – there was never anything to debunk since they didn't measure their construct and therefore did not carry any knowledge about the hypothesis.)

Similarly, in the Lewandowsky case, we already knew, in advance, that climate skepticism isn’t going to be driven by beliefs like moon-landing hoaxism. We have survey data from professional pollsters. We know that very few people believe the moon landing was a hoax. And we know that a much larger number of people are skeptical of climate science. A recent survey showed that 7% of people believed the moon landing hoax, and 37% thought climate science was a hoax. Best case, 19% of climate hoaxists are moon-landing hoaxists (7/37). (It will be lower, because they won’t completely overlap.) If the vast majority of climate hoaxists/skeptics are not moon-landing hoaxists, which we already know from the polls, there’s no point trying to link them. Unless you’re trying to get at a specific cognitive process (which you won’t do with Lewandowsky’s crude surveys) or a subcategory of climate skeptics, there’s no point. It’s scientifically specious, and I think unethical, to draw links between a group and some far-out, damaging belief when the vast majority of the group simply does not hold that belief.

So in both cases here, reality constrains our research, our hypotheses, in ways that are not readily acknowledged in the field. We’re not granting logic and exogenous facts the authority that they truly have. Our research can be rendered meaningless, or deeply flawed, by facts available outside the field, facts that do not count as “data” by our normal reckoning, in concert with plain logic.

Category 3

The third category is how we deal with the validity and reliability of scales (surveys). I’ve pointed out to researchers that the Social Dominance Orientation (SDO) scale might be invalid, only to have them respond “But it’s very reliable.” Reliability has nothing to do with validity (well, it actually does, but not for our purposes here.) But reliability comes as a number. As we’ve noted, people like numbers, data. Reliability can be expressed as a Cronbach alpha, or better yet, McDonald’s omega. Validity doesn't come as a number, as “data” (at least not in simple form, in one statistic.) Validity often requires logic, substantive reasoning about what things mean. The SDO is what I call a Caricature Scale. I’ll dig into this much more in an upcoming methods paper, but a Caricature Scale has specific features. Ultimately, they’re traps. They’re very reliable traps, in more ways than one, but they’re not generally valid, and are artifacts of a primitive early stage of social science. The SDO, for example, is written purely in the language of the American academic left (circa 1990.) It’s groups this and groups that, full of caricatured and straw man versions of vaguely conservative positions. It traps conservatives into endorsing the midpoint, perhaps out of confusion, while liberals endorse the low end. Voila! Conservatism is correlated with “Social Dominance Orientation”, even though many or most of them don’t actually endorse the items.

In summary, scientists need to be capable epistemologists. No data is inherently meaningful, and it’s quite easy for it to be meaningless. People are looking at r's and p's, without looking at the data and considering what it means. In our "data, data, data!" mindset, we're not paying enough attention to meaning, and ultimately, to reality. We need to understand that validity is decisively important, that invalid science is ultimately not science at all. We need to understand the epistemic authority and utility of logic and reasoning. We need to be more firmly seated in the sciences, as opposed to the humanities. We need to understand that many published papers do not represent or carry knowledge, at least not the knowledge claimed, and some papers are just false. We need to join our scientific cousins in other fields, and account for these realities by retracting such papers when we discover them. If we don’t, we risk accruing a large number of false claims and findings, which would strain our standing as a science.

This article was originally published on August 18, 2014, when I was a graduate student, on my site joseduarte.com.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

Ten years ago, the Daily Beast published an article headlined Are Homophobes Mentally Ill? Science Says ‘Maybe’:

According to findings recently published in the Journal of Sexual Medicine, if you harbor homophobic views it’s likely you may also endorse some of the following dysfunctions: misogynism, psychoticism, uber-religiosity, immature coping mechanisms, [discomfort] with close relationships, and above-average levels of anger, aggression, hostility, and hyper-masculinity.

Of the nine claimed associations, seven are false. In fact, five of the listed variables do not exist in the study. Here are the study results:

Look at the significance column on the right. Psychoticism and immature coping style were the only true effects. At least in the desired direction – as you can see, neurotic coping style and depression were negatively correlated with “homophobia”, meaning that the higher people were in “homophobia” the less depressed and neurotic they were. Daily Beast withheld these two findings.

Discomfort with close relationships (see RQ – Relationship Quality) and hostility exist in the study, but they were not associated with “homophobia”. The author fabricated these effects.

Misogynism, “uber-religiosity”, anger, aggression, and “hyper-masculinity” do not exist in this study. The author fabricated these variables outright, and their association with “homophobia”.

I emailed the Daily Beast twice shortly after this article was published and explained the false claims, that most of the variables didn’t exist in the study, etc. They never responded or corrected. I contacted author Zachary Siegel on Twitter – he didn’t respond.

Ten years later, the article is still up, uncorrected. Not only that, they slapped “Diseased” in the title bar, explicitly trying to evoke a disgust and dehumanization response toward these purported “homophobes”.

The study is also false

The study didn’t find any “homophobia”.

It’s not just that “homophobia” doesn’t exist — it’s an Orwellian leftist smear word that asserts an irrational or clinical fear of homosexuals that is not evident in the population.

No, I’m saying that even if we take the researchers’ measure seriously, they didn’t find any homophobia.

This was a 2015 study of 551 Italian college students. The researchers translated the Homophobia Scale published by Wright, Adams, and Bernat in 1999. It has 25 items using a 1-5 scale from Strongly Agree to Strongly Disagree.

The researchers said they summed the scores on the 25 items. Higher scores mean more “homophobia”. Here’s their table of basic stats:

See the problem? The average score for women is impossible1, because the minimum possible score is 25 — that’s if you answered 1 to every item. This is an error of some kind, which potentially invalidates the study.

And the men are near the floor. The possible score range is 25 - 125, so a 35 means answering 1 to most items.

Let’s pause and clarify what the responses mean. The options are:

1. Strongly agree

2. Agree

3. Neither agree nor disagree

4. Disagree

5. Strongly disagree

(I’m not sure why the scale is pointed in an atypical direction, with max agreement set to 1 instead of 5.)

An example item is “Homosexuality is immoral.” (This would be reverse-coded when computing the score — a 1 becomes a 5 and so on, so that higher scores denote more “homophobia”.)

That’s a philosophical, moral, possibly religious position. It does not describe any sort of fear of homosexuals.

But note also that you could answer Disagree (would be converted from a 4 to a 2) and this would count as an increment of “homophobia” using their linear correlation method — simply because you didn’t choose Strongly disagree.

And if you answered “Neither agree nor disagree”, you’d be hit even harder.

Consider another item: “I enjoy the company of gay people.”

If you don’t answer Strongly agree, you’ll jack up your “homophobia” score. I’d probably answer “Neither agree nor disagree”, netting me two “homophobia” points.

(The scale makes no distinction between gay men and lesbians, making it less useful since people tend to view them differently.)

Now reconsider the men’s average score of 35. Can you see how easy it would be to score 35 even while never endorsing a single “homophobic” belief? Just being the kind of person who avoids the extremes on surveys, who chooses normal agreement/disagreement instead of “strongly” will net you at least a 50.

A tendency to endorse “homophobic” views would get you up around 100 out of the 125 max. Again, the men averaged 35 — there was no “homophobia” here.

Data

I emailed lead author Emmanuele Jannini and asked for the data.

He replied with one word: “Why?”

A bit annoyed, I replied “So I can analyze it.”

No response. He never sent the data.

That’s common in academic “science”. Social psychology in particular is full of fraud, false claims, and secret data.

The trick

The researchers used a trick — arguably a form of fraud — common in academic psychology research.

They used linear correlation to link X and Y even though there was no X (“homophobia”) and there might not have been any Y — the “mental health” variables in the table, for which they didn’t provide the averages, which could’ve been as low as the “homophobia” scores.2

They were able to do this, to link “homophobia” with these other things, because correlation only requires variance, not substantive standing. So you can have scores clustered at the bottom, as in this study, but the variance from say 25 to 45 on a 25-125 scale is enough to get you some significant correlations if you try lots of variables, as they did.

Just create a response scale with “strongly agree” and “agree” and voila!, you now have variance, even though it’s the same substantive position. Same with “strongly disagree” and “disagree”.

Adding a midpoint like “neither agree nor disagree” allows you to treat confusion or no opinion as a chunky increment of whatever you’re claiming this is — in this case “homophobia”.

Academic social psychologists love to defame and harm conservatives and non-leftists, and this is a key trick they use.

For example, they often falsely link bizarre caricature scales like Social Dominance Orientation and Right-Wing Authoritarianism to conservatives. Critically, conservatives don’t tend to endorse the crazy items, but they might endorse mundane items like “The "old-fashioned ways" and the "old-fashioned values" still show the best way to live” from the Right-Wing Authoritarianism scale. So they cluster around the midpoint, but since leftists cluster at the floor, there’s enough variance to drive correlations.

The end result is that false claims that conservatives are high in “social dominance orientation” or “authoritarianism” are reported in major media outlets, even though conservatives do not in fact score high on those scales and do not endorse views matching the labels — they just score higher than leftists.

Biased social psychologists can call these scales and constructs whatever they want, so they choose names like Social Dominance Orientation to defame and pathologize conservatives and associate them with views that are alien to them.

Mental health

Just as there was no “homophobia”, there might not have been any psychoticism, hostility, etc. The researchers didn’t provide the scores for the mental health variables. The first table I showed you included only inferential statistics and p-values. The second table included descriptive data — average scores and standard deviations — but only for the “homophobia” scale.

Nowhere do they disclose the scores on the mental health scales. This means that their sample might have been clustered at the bottom of some of those scales, just as they were for “homophobia”.

And what were these scales? Well, an example item from the psychoticism subscale3 is “In the previous week, how much were you bothered by the idea that something serious is wrong with your body.”

The bolded part was the stem for every item. The response options were a 5-point range from Not at all to Extremely.

If you answered A little bit, that gets you a point of psychoticism. Choosing Moderately nets you two points.

Now’s a good time to remind you that the sample here was college students, like so much of the junk that academic psychology researchers publish. College students are a terrible choice — a terrible phase of life — to be asking about body anxiety and treating it as a component of “psychoticism”.

The researchers didn’t disclose the mental health scores. Also, they didn’t provide any of the scales, not even the “homophobia” scale. You cannot read the journal article and know what they did, what they measured. This too is a common issue with academic psychology research — they don’t fully disclose their methods. If you can’t see the scales — every single item and the response options — how can you possibly assess or interpret what they found? (The reason I was able to cite specific items from the various scales is that I dug them up — they’re not in the journal article.)

The upside down

It’s disturbing that Daily Beast slapped “Diseased” in the title bar, and it highlights the malevolence of so much of leftist media culture. These are extraordinarily bad people, and remember, the effects were not only false, but Daily Beast fabricated most of them!

Jannini, the lead researcher, told the media: "The study is opening a new research avenue, where the real disease to study is homophobia.”

Unbelievably unethical and unscientific, especially since he didn’t find any “homophobia” and refused to share his data.

His comment highlights something we missed — concocting the term “homophobia” was the initial step in pathologizing normality. It’s a clinical-sounding pathology term, but no one discovered a clinical fear of homosexuals in the population (at least not in, say, even five percent of the population). It’s a fake label, and it’s unethical for journalists to use the term descriptively.

Labeling someone’s views as a phobia is insulting, and leftists likely landed on this smear because they thought it was especially insulting to normal, masculine men to say that they were terrified of gays, that they were scared of something. They quickly broadened this tactic by concocting more fake phobias — Islamophobia, transphobia, and even fatphobia.

During the Biden administration, major officials like Secretary of State Anthony Blinken posted scolding reminders of the “International Day Against Homophobia, Biphobia, Interphobia, and Transphobia”. Not wanting to look it up, I assume that “biphobia” is supposed to be a pathological fear of bisexuals, as opposed to having been diagnosed with two distinct phobias. (Picture people fleeing bisexuals like a Godzilla movie.) I have no idea what “interphobia” might be. This in your face obsession with sex, self-absorption, and forced valorization of abnormal sexual behavior and mental illness is a historically unprecedented debasement of American culture.

Something dark is happening with efforts to make normal people feel like they’re mentally ill or defective, and hostile strangers telling them what they feel inside — usually fear and “hate”. A key element of “woke” is to invert the normal and abnormal, beauty and ugliness, health and disease. This subversion is what it means to “queer” something in leftist parlance.

I’m particularly worried about boys and young men who are inundated with psychologically invasive propaganda about homosexuality.

They’re told there’s something wrong with being disgusted by homosexuality. There’s nothing wrong with it — the large majority of men are repulsed by the idea of being with a man, and humans are naturally disgusted by feces and therefore anal sex. We wouldn’t be here today if men weren’t drawn to women and if humans didn’t have a disgust response to feces and similar pathogen threats.

Now, we can choose to build a higher-order ethical framework such that we’re not morally disgusted by gay men, and are tolerant and benevolent toward them. That will depend on our core ethical framework. In any case, I’m not endorsing hostility toward gays here.

Boys are also told that natural feelings of disgust or expressing “homophobic” views are signs of suppressed homosexuality. False. (One or two cases of prominent Christian leaders being exposed as having gay affairs energized this narrative — people missed that this was a tiny percentage of such figures.)

In the ten years since the Daily Beast article, gender ideology has swept our institutions, and we’ve seen efforts to gaslight straight men into thinking they should be willing to have sex with other men if those men “identify as” women (and to pressure lesbians into performing oral sex on men who “identify as” lesbians, as The Australian reports). For example, see the ABC drama Big Sky, the most Orwellian thing I’ve seen on TV. A straight US Marshal in Montana faces a romantic dilemma — should he be with this beautiful woman or… a man.

The hitch is that the Marshal perceives this trans-asserting man to be a woman. Everyone on the show does. Note that the Marshal wants a serious relationship, potentially marriage, and he’s acting like he doesn’t know the man is a man, like he’s got a nail stuck in his head. It’s a complete mind wipe, like an episode of The Twilight Zone. He’s looking at this man and seeing a woman. What would happen in the bedroom? Would he see the other man’s penis and understand that he’s a man, or would he just redefine “woman” to include the penised? Who knows?

To really rub it in, the writers made the other man a truck stop prostitute… The Marshal met him in the course of an investigation. It’s a perfect “queering” of the norms of civilization, Christians, baseline humans, and of straight US Marshals in Montana. Leftist media and academics want you to think you’re mentally ill if you see that man as a man and if you’re aghast at the idea of the Marshal settling down with this man and former prostitute who he’s made himself think is both a woman and marriage material.

It’s obvious what’s happening here. These attempts to pathologize normal people and their attitudes toward homosexuality and trans ideology are driven by insecurity. These people are deeply insecure in themselves and their behavior. That’s why they have to flood every institution with their propaganda, even indoctrinating elementary school children to think they have a “gender identity” separate from their sex — they want to create more of themselves, and in so doing, legitimize themselves.

They want acceptance, validation, even valorization. Nothing doth protesteth as much as a “pride” parade. And maybe we should give them acceptance, but we shouldn’t tolerate lies and fraud used to gaslight normal people into thinking they’re mentally ill.

I’m especially concerned about boys and young men being brainwashed and tormented by this sort of propaganda. Parents — this is another reason to keep your kids away from screens, social media, and public schools (and some private schools). Let your boys grow up healthy, and in peace.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

Glenn Kessler, the Washington Post’s outgoing “fact checker”, awkwardly claims to be a voice for facts and truth. In fact, he’s published countless false fact checks, including examples we can classify as fraud — that is, deliberately, knowingly false.

On June 28, 2024, Kessler published a fact check of the Biden-Trump debate. He included the following Trump claim:

“The only jobs he created are for illegal immigrants and bounce back jobs that bounce back from the Covid.”

In his fact check, Kessler says:

This is false. Biden’s jobs record in his first three years certainly tops Trump’s performance. In the first three years of Trump’s term, about 6.5 million jobs were created — less than half the number created under Biden in the same time period. The number of jobs is now 6.2 million higher than the peak under Trump in February 2020, before the pandemic struck the economy.

Meanwhile, employment for the native-born population has increased by almost 6.8 million under Biden, according to the Bureau of Labor Statistics. (We start from February 2021, the first full month that reflects employment under Biden.) Employment of foreign-born workers increased about about 5 million from February 2021 though May, the bureau says. The agency says this figure includes more than just undocumented immigrants; it also includes legally admitted immigrants, including refugees, and temporary residents such as students and short-term workers.

Remarkably, Kessler’s links confirm Trump’s statement, but he conceals this and few readers will notice because they won’t check the links.

Let’s anchor ourselves in the original claims — a critical habit when dealing with fact checkers. Trump said the only jobs Biden “created” were for 1) illegal immigrants and 2) bounce back from the pandemic.

Kessler starts by referring to the number of jobs created under Biden, which is irrelevant to Trump’s claims. He continues by comparing the total number of jobs to the peak under Trump, which is also irrelevant. Trump’s claims were about the nature of the job creation.

In the second para, Kessler notes the increase in jobs for the native born, but this is the very bounce back Trump noted. This is what Kessler linked to:

As you can see, there was no net increase in native-born employment – it was just a bounce back. (I placed the orange arrow at May, 2024.)

It was incredibly bold of Kessler to link to a graph that so plainly validated Trump’s claim — like he was daring someone to notice.

So there was no net increase for native-born, and yet an increase in total employment. Therefore, the latter was driven by immigrants. And we separately have his admission that immigrant employment increased by 5 million. This is what he linked to:

Notice the difference in the graphs? Native born employment did not increase from pre-pandemic levels, while foreign-born employment increased dramatically. What share are illegal immigrants is unclear. Most immigrants in recent years were illegal, and I also suspect the 5 million figure is an underestimate. (The mismatch between the 5 million foreign-born and 6.2 million net increase is because Kessler switched periods.) The only potentially false claim Trump made is that the net job growth was from illegals specifically, but we’d need the data to know.

Again, Kessler linked to these graphs! He was toying with his readers, brazenly lying by saying “This is false” about Trump’s true claims, then padding his fact check with irrelevant filler text, and yet disclosing that foreign-born employment increased by 5 million, which would seem to undermine his point.

This phenomenon of “fact checkers” claiming that something is false while refuting themselves in the body of their own fact check is quite common, but I’ve never seen an example of linking to graphs that so cleanly falsify the fact check and validate the claim under review.

Note also the invalidity of the methods leftist fact checkers use with debates. They allow themselves to pick and choose which claims to fact check, which introduces the potential for selection bias, a bias that is always obvious with Kessler. In the Trump-Biden debate, Biden claimed that no soldiers had died on his watch, an unbelievable, shockingly false claim I don’t think any President has ever made, one that would seem to disrespect those soldiers who died in the incompetent Afghan withdrawal, among others.

Kessler didn’t include Biden’s whopper. Readers would have no idea that Biden made such a gravely false claim. Rather, Post readers came away with the false impression that Trump lied about job creation under Biden.

This false fact check is in the Post’s repo of Trump’s “false and misleading claims”, yet Trump’s claims were accurate as far as we can tell. That repo is a fraudulent product full of false claims of falsity, including the funniest fact check of all time.

At some point we need accountability for these people. Leftist media outlets like the Washington Post, NYT, CNN, et al. make lots of false claims. They’re unreliable and unethical and have done severe damage to American democracy and left journalism in tatters. The “Trump lies constantly narrative” was powered by fraudulent fact checks like this one — Trump says something true, yet a self-styled fact checker swoops in and says it’s false, even that he “lied”.

At this point, most of the unfavorable fact checks of Trump might themselves be false or invalid. Conservative and libertarian funders should consider funding a thorough, rigorous data collection and analysis effort. We need to determine and document the false claim rates of every major media outlet as well as the fact check groups and make the data and stats open in an online repository. We also need to properly measure various forms of bias to fully characterize these outlets. With that data we’ll have good tools and evidence for applying pressure, eliciting reform, marginalizing these outlets as appropriate, etc. We need to vaporize the premise that these major media outlets are reliable and credible. Scroll up again to see what they do.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

“Leaders must see reality as it is, not as it ought to be.” — Benjamin Netanyahu

If major media outlets falsely accused you of slaughtering 60 hungry people in line for food, you’d have something to say about it, right?

You’d say it was false, for starters. If you’re a human being who lives on this planet and intends to continue doing so, you can’t have the world thinking that you slaughter hungry people in line for food.

Since the Israel- and US-backed Gaza Humanitarian Foundation (GHF) set up food aid centers in Gaza in May, Israel has been falsely accused of slaughtering scores of Gazan civilians in line for food too many times to count.

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Each story is planted by Hamas, which opposes the GHF aid effort since it can’t control and profit from it as it does with the UN program.

Major leftist media outlets have, incredibly, run the stories without any apparent hesitation. There’s never any video of these incredible-sounding massacres, and there surely would be if they had happened. Note that in Israel’s 77-year history, Israeli troops have never massacred dozens of civilians. They’ve never had a My Lai-type incident (Vietnam) or accidentally bombed a wedding like the US did a couple of times in Afghanistan and Iraq. It would be revolutionary, a huge development, if Israeli troops ever slaughtered dozens of civilians.

The scene of a slaughter of 50-100 people would be horrific. To kill that many people, Israel would have to shoot 200 (wounded always outnumber the dead). There would be no way to hide it. Countless smartphones would record it, much like the Hamas carnival of evil at the Nova peace concert on Oct 7, 2023. Satellites could see it. Someone would have to take away the bodies. All of which is to say the stories are obviously false, even if we didn’t know that Israelis aren’t monsters.

Despite all those obvious facts, the media credulously reported the latest massacre story from Sunday, July 20:

They all cite the Hamas “Health Ministry”. (The “medics” in the Reuters headline turns out to be the Hamas Health Ministry, not blood-soaked doctors at the scene.)

The IDF response, per NPR:

In a statement, the Israel Defense Forces said its troops encountered thousands of Palestinians gathered in northern Gaza and "fired warning shots in order to remove an immediate threat posed to them."

The Israeli military said it was examining the incident but that an "initial review suggests that the number of casualties reported does not align with the information held by the IDF."

This is the standard IDF response to all these stories. They fired warning shots. They’re looking into the claims regarding casualties (the claim being that they slaughtered nearly 100 people looking for food).

That’s it. It’s not just inadequate – it’s insane. It’s practically suicidal for a very small country to respond so incompetently to claims that it slaughtered desperate people in line for food, over and over. Israel will not survive this century if it doesn’t drastically improve its communication and transparency. A small country simply has to care about what the world thinks. It’s not optional, especially if the world thinks you slaughter civilians in line for food.

In every case, Israel should say “This is false. We did not slaughter nearly 100 people seeking aid. This is Hamas propaganda, and no ethical media outlet should ever report a terrorist group’s propaganda. Here is what happened…”

An example of “what happened”:

“Our soldiers were stationed 500 yards from the XYZ aid center. They maintain a 100 yard restricted zone around their position. A group of people approached their position, entering the restricted zone, NOT seeking aid, which was 400 yards in the other direction. Our soldiers shouted warnings to back off. These people ignored the warnings and kept approaching. Our soldiers then fired warning shots over their heads. These people kept approaching. Then our soldiers shot two of them, killing one. This bears no resemblance to “Nearly 100 people killed seeking aid.”

Instead, Israel just says they fired warning shots and that they’re looking into it. By the time they finish looking into it, many millions of Americans and Europeans will believe they slaughtered 100 people in line for food, and the vast majority of them will retain that belief even if their favored leftist media outlet runs a correction days later.

This is a serious problem. Israel is a very small country. It cannot survive ostracism by the West. For one thing, Israel depends on imports from many countries, including American military hardware. It has very little in the way of natural resources, and again, it’s a small country – the size of New Jersey both in land area and population (9.5 million). The Israelis are brilliant – they made the desert bloom and are the most compelling birth-of-a-nation story since America’s founding. But no one can survive alone in this world.

Israel exacerbates the problem by banning journalists in these areas. We could never trust any authority or country – even those we support – with the power to ban journalists. I’m not interested in excuses about safety or bias. Safety is the journalists’ problem, and bias is both a severe issue already and not an excuse anyway. It’s a terrible policy and even biased journalists would’ve been able to report that there was no slaughter at food aid centers.

From the Sunday articles, I’m piecing together that there’s a restricted area up north and that perhaps Hamas sent a group of people to deliberately breach that area. This looks like the standard tactic from the other hoaxes.

1. Hamas sends a group of people — call them bunnies — to approach a IDF troop outpost, entering the restricted buffer zone around the outpost. These aren’t people in line for food.

2. The IDF troops warn them to back off.

3. The Hamas operatives keep coming.

4. The IDF troops fire warning shots. That is, shots that don’t hit anyone, since any soldier can reliably aim an M4 over people’s heads or a few yards in front.

What happens next is unclear in these incidents. If the Hamas operatives keep coming, one or more of them gets shot.

If this happens a few hundred yards from a food aid center, or even a kilometer away, it gets reported as “(spin a wheel for the number) Gazans killed by IDF fire at/near food aid center” without evidence, video, nothing.

Unlike the previous hoaxes, the Sunday story doesn’t refer to a food aid center. Israel might have taken steps to make the food aid center hoaxes harder — e.g. cameras, drones, etc. Instead, the media just says “as they waited for UN aid trucks” or “while seeking aid”. These are vague, almost meaningless descriptions because waiting and seeking aren’t real activities — Hamas figured out how to continue the food aid massacre hoax without needing any aid to exist in the area. They can just say their people were seeking aid, or waiting for it. For all we know, they’re describing a battle between IDF soldiers and Hamas forces.

In any case, Israel needs to be transparent and energetic in its response to these stories. One excuse I hear from Israelis is that there are two billion Muslims in the world, compared to only 15 million Jews, and that they spread hoaxes online at a scale Israel can’t deal with. But I’m not talking about that. I’m talking about stories run by major media outlets like CNN and Reuters. Israel needs to address and vaporize a couple of hoaxes a day at two dozen media outlets. That’s a team of four or five people.

Strategy

I recommend:

1. Put plenty of cameras around the food aid centers and make the feeds public and live on the web. This is cheap. Throw in some drones circling these areas, with public online feeds. Also cheap. Just completely eliminate the possibility of successful hoaxes pertaining to the centers themselves, or even within a 1 mile radius. (The drones give you the ability to respond to a Hamas shooting nearby and focus on it, unlike fixed cameras.)

2. Put internal reporters with all units and outposts where shootings, even warning shots, could happen and have happened. You know Hamas tactics. You know they’ll sending bunnies into restricted areas to trigger warning shots and more. Like the food aid centers, plaster these areas with cameras and drones.

3. When Hamas fabricates a hoax about the IDF slaughtering people, clearly and thoroughly communicate what happened within an hour of the media queries. It’s insane to say stuff like that an "initial review suggests that the number of casualties reported does not align with the information held by the IDF."

   
   The IDF should say exactly what happened, where it happened, when it happened, whether they shot or killed anyone, who they were, and what they were doing. Provide maps and video. At this point all IDF activity should be recorded.

   
   Talking about “warning shots” is useless unless that’s all that happened. Many people will assume that the massacre actually occurred if Israel only says they fired warning shots, but doesn’t also say “We didn’t actually shoot anyone.”

The fact that Israel has dominion over these areas and bars journalists imposes a heightened responsibility to provide clear information about what is happening. The policy barring journalists should be reversed, but in any case, Israel needs to communicate clearly and swiftly. There’s no excuse for a 12-hour delay. Fix the process. Put people in place. Communicate clearly and swiftly.

Take on the media

We have a serious situation with legacy leftist media acting as Hamas PR. It’s inexplicable, a deeply disturbing manifestation of their severe left-wing bias. This is a problem for America independent of the implications for Israel.

There’s no reason to just passively accept this reality. Fight it.

How?

First, by fully and rigorously documenting it. It’s no good to just complain about their pro-Hamas bias every once in a while, or to point to a random headline or story once a week on X. The message that legacy leftist media is acting as Hamas PR and making wildly false claims needs to be focused like a laser.

The best way to do that is with unimpeachable data. Build a team. There’s an opportunity here to expose these outlets in a way that can’t be easily dismissed by leftists and politicians.

Take all of their Israel and Gaza coverage – all of it, at every major outlet – and go through it, looking for false claims and several forms of bias. Aggregate all of this data and generate useful metrics and statistics for each outlet. Put everything on the web, with open data and handy visual and interactive tools.

Deploy a tool where people can click on Reuters, the Washington Post, NPR, or any other outlet and see a full inventory of all their false claims, all their blood libels. Show how, less than a month after Oct 7, the New York Times and other leftist outlets falsely reported that Israel bombed a hospital and killed 500 people, when it turned out that a defective PIJ terrorist rocket (intended for Israeli civilians) hit the hospital parking lot, killing 0-50 people. Israel will be able to present dozens of hoaxes like this.

With clear, unimpeachable data, Israel can do a few things to hold these media organizations to account. Israel should also investigate antisemitic incidents like the Jew killer in DC and the Jew flamer in Boulder to see if they can be traced to specific hoaxes pushed by legacy media.

What Israel is doing now isn’t working. The West is turning against Israel because the media keeps reporting Hamas hoaxes, and Israel is making it easy for them to get away with it. Two Israelis were just arrested in Belgium essentially for the crime of being Israeli. Things will get worse from here. Instead of communicating clearly and debunking these hoaxes, Israel is leaving it to a handful of X accounts to investigate Hamas death totals, expose BBC hoaxes (e.g. David Collier), identify some major media “journalist” as a Hamas supporter or member, etc. That’s no good — the Israeli government should be doing all this and more.

I’ve heard from Israelis that people won’t believe hoaxes like the food aid center slaughters because they’re so ridiculous. I think they’re wrong. It’s a huge mistake for Israeli leaders to assume that Americans and Europeans who see these headlines from Reuters, CNN, BBC, AFP, Sky News, the Washington Post, NPR, the New York Times, NBC News, ABC News, CBS News, etc. will not believe them, especially after seeing ten of them in twenty days.

One leftist President of the United States could be devastating to Israel, especially if they’re animated by these hoaxes, which at this point seems likely. Israel needs to deal with this epistemic sewer before it’s too late.

Joe Duarte grew up in small copper mining towns in Southern Arizona. He earned his PhD in social psychology, with his dissertation on envy and antisemitism, and now focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

Everytown is a leftist NGO founded by Michael Bloomberg that lobbies for gun control, including laws that disarm peaceful, non-criminal citizens.

They created a state-level Gun Law Strength index, and claim that states with better scores on this index have lower rates of “gun deaths”, or sometimes “gun violence”. They use both terms interchangeably, but their outcome variable is gun deaths, not gun violence — it’s all deaths from gunshots, including suicides and accidents. Most gun deaths are suicides — 58% in 2023, while 38% were murders, and ≈1% were accidents. (Everytown also includes legal self-defense shootings and police shootings. I suspect that when people read “gun violence” they think only of criminal violence, and that most uses of the word violence are understood as inherently criminal.)

Problems:

1. Their data is secret — they won’t release it.

2. Their variable construction is secret. They won’t release that either.

3. They offer no evidence that any of the laws they include in their index reduce “gun deaths” — whether suicides or murders by gun. That is, they have no apparent justification for including them.

4. They control for nothing in their correlation between their secret Gun Law Strength index and gun deaths. Not race nor any of the other known predictors of suicide and murder rates. Literally nothing – it’s a Pearson correlation.

Note that each of these problems by itself vacates their claims. There’s nothing we do with research of this quality, and since they won’t release their data we have no reason to assume it even exists.

I pointed to the first two issues above in a post on X. NYU social psychologist responded with this:

Strangely, nothing I said was false. (The false claims by Knowles and Van Bavel that I refer to in the X post are detailed in this report.)

Let’s start with the data. It’s secret. Everytown won’t release it. Knowles didn’t find it or anything. So his claim that my claim was false was itself blatantly false. (He does mention that Everytown says that they get their “gun deaths” data from the CDC, but I have no reason to care about what they say — they won’t release their data, that’s only their outcome variable anyway, and Knowles failed to notice something else.)

What about their methods? Everytown doesn’t disclose them, not to the extent that we’d be able to reproduce them, which is the standard in science. Knowles cites this text as falsifying my claim:

Okay, go reproduce their study and come back with your results.

You can’t do it, of course. What is this text? It’s a description of levels and point ranges, with various laws assigned to different levels. (From their website here.)

How many points does a state get if it has a Stand Your Ground law? No idea. The range is up to 6 points. Does it get 6? Or 5 or 4? What kind of law merits 3 or 5 or 2 points?

What does “mental health record reporting” mean specifically, and how does a state get 1.5 points instead of 1.0 points?

In fact, what do any of these things mean specifically? We have no idea. And what’s the scoring rubric? We have no idea. (A rubric would tell raters how many points to give to a particular law within the specified point range, e.g. some background check laws would count for 6 points, while others would count for 5 or 4 points. Their index method would still be invalid — I’m just noting that we have no idea what they did.)

And we can’t see what score Everytown gave each state on these criteria, or why. We can’t see anything.

So no, they didn’t describe their methods. Not to the standards of scientific research, which is that methods are described in sufficient detail that anyone can reproduce the research from the description. There’s nothing we can do with this.

An index of laws is invalid for demonstrating causal effects or even correlations because:

1. Laws aren’t commensurable units, nor do they have obvious mathematical/numeric assignments.

2. The choice of laws can be trivially rigged.

3. The rubric and rating system can be infinitely rigged.

4. Any real effects of a law are buried in a bucket of other laws, and it wouldn’t be clear what an effect for an index of dozens of laws meant.

Secret ratings + a secret method = we walk away. This isn’t anything, certainly not valid research.

Everytown could easily rig their index to get the results they want. For example, perhaps giving six points to Stand Your Ground laws and only one point to waiting periods helps get the correlation they want, whereas the doing reverse weakens the correlation. I strongly suspect that their scheme of placing different laws into four “levels” with different point ranges was engineered to get the correlation they want, and is fragile to any changes. Especially since they offer no explanation or theory for the four levels, the point ranges, the choice of laws, or the placement of specific laws in a given level.

“Gun deaths”

“Gun deaths” can fall while the suicide and murder rates stay the same, or even increase. Why? Because gun deaths isn’t composed of the suicide and murder rates — it’s composed of suicides and murders by gun. “Gun deaths” is a mindless variable that I suspect most people interpret as murders, perhaps visualizing school shootings.

It’s easy to be fooled by “deaths” and to not realize that with gun deaths, we don’t get:

1. The murder rate (just murders with guns, hiding any shift in methods)

2. The suicide rate (just suicides with guns, hiding any shift in methods)

3. Crime rates

4. Defensive uses of firearms (1.7 million per year according to English, 2021)

5. Any account of rapes, assaults, robberies, etc that were prevented by victims brandishing firearms, because they don’t go into this bucket of death

Moreover, the premise of this bucket of mostly suicides is that all those suicides should be prevented. It would never occur to me that it was my place to judge thousands of suicides, lives, and contexts I know nothing about. I don’t have a settled view of the ethics of suicide, but I’m sensitive to context and the possibility that someone’s suffering is horrendous, inescapable, and their reasons immovable. In any case, we have no reason to suppose that laws regarding concealed carry permits, open carry, guns in schools, or high-capacity magazines have any effect on suicides by gun, much less the bottom line suicide rate.

It’s easy to be deceived and carried along by Everytown’s “gun deaths” variable if you don’t know what it is, or pause to think it through. The gun deaths we’d care most about are murders, since alternate methods of suicide are easy and we might not want to prevent all suicides anyway. And we don’t just care about murders by gun — we care about all murders, the total murder rate.

That’s the bottom line — the murder rate — not “gun deaths”, since if gun deaths fell but the murder rate stayed the same or increased, that’s not a win.

In summary, Everytown has two variables — a predictor and an outcome.

The predictor is an index of gun laws rated secretly according to a secret rubric giving more weight to some laws but not others, with no evidence that any of the laws reduce suicide or murder rates, and no scientific explanation for the rating system.

The outcome variable is just a bucket of gun deaths, mostly suicides, uncoupled from the overall suicide and murder rates, and from crime. No account is taken of the benefits of gun ownership or the potential benefits to people who don’t currently own them.

Their data for both variables is secret — they won’t release it. This suggests it either doesn’t exist or that their convoluted index is rigged to get the crude Pearson correlation they want.

In his X thread, NYU’s Knowles said that Everytown “runs a tight ship”. This strengthens my impression that we have a profound issue with the quality of evidence and research that leftist academics think humans should be satisfied with. Not only satisfied with, but used to justify the mass coercion and authoritarian governments that leftists champion. Research without data, said data being held in secret, based on mystery synthetic variables that are inherently invalid, and a deceptive bucket-of-deaths outcome variable that bears little relation to the outcomes we care about. It’s dangerous for civilizations to make decisions based on research of this quality, and academics promoting secret data junk research from partisan groups are properly viewed as partisan activists, not scholars of any description.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

In a recent Nature Medicine article titled “Political Polarization and Health”, NYU faculty Jay Van Bavel and Eric Knowles, with collaborators from Syracuse and Columbia, claimed:

“People who live in states with more liberal social policies, such as generous Medicaid coverage, higher taxes on cigarettes, more economic support (for example, minimum wages) and more firearm regulations live longer⁴¹ than their counterparts in states that embrace more conservative policies.”

Their source (the 41 footnote) is an essay by Jennifer Montez (2020) that includes a table with those variables for two states – New York and Mississippi. It’s just a table with some descriptive stats, like the number of gun control laws (75 vs. 5), the minimum wage level (no correction for cost of living), and life expectancy (which are incorrect per her source).

There’s nothing we can do with a table with bits of data for New York and Mississippi. That’s not anything. We can’t make sweeping claims about state laws and life outcomes based on two states. Moreover, none of these claims account for race, which is a major predictor of lifespan, gun violence, health habits, and so on. Mississippi has the largest black population share of all states at 38%. To talk about any of these variables without controlling for race is scientifically incompetent and a waste of everyone’s time.

On X, I notified Knowles and Van Bavel about their false claims. Knowles replied that I hadn’t read the rest of the Montez essay.

The essay has no analyses or models. There are no statistics in the text, or any other tables. It’s an essay, not an empirical paper. There are some sweeping claims in the text, but they’re false (see below), so no, their source is invalid and their claims are false in that there’s no support for them.

Remarkably, Van Bavel and Knowles made their false claims causal by placing them under the heading “How does polarization impact public health?”, and the paper is saturated with causal assumptions. “Polarization” to them means the existence of conservatives, libertarians, moderates, and other non-leftists — i.e. the lack of leftist hegemony over America. It’s quite Orwellian — when they say polarization harms health, they mean that not having all their preferred authoritarian leftist policies in force in all fifty states harms health.

Knowles later (on X) retreated to claiming that a different paper — Montez, et al. (2020): US State Policies, Politics, and Life Expectancy — backed their claims, even though they didn’t cite it.

That’s an actual empirical paper, but it doesn’t do anything for us. It’s not clear what the researchers did because they don’t say, but the only analyses of life expectancy are for men and women separately, they didn’t model the number of gun laws at all, or the minimum wage level and Medicaid coverage specifically, and there’s no effect of their “gun control” variable on male life expectancy, just female, which looks impossible given the data (below). The study is invalid (also below), so there are no effects we can take from it, it doesn’t match the claims Van Bavel and Knowles made, they never cited it, and their claims were drawn from the table in the Montez essay that they did cite.

Strange exclusions

Since the the claims are false, they must be corrected. They’re central claims in the paper, which is a remarkable artifact — they only attempt to show the benefits of authoritarian leftist policies. They don’t present any evidence for the harms of such policies or the benefits of non-leftist approaches, and they didn’t bring in any non-leftists, conservatives, libertarians, etc who could’ve countered their biases and improved the quality of the paper. There’s no attempt at rigor, balance, or weighing of evidence. There’s no evaluation of evidence quality, and they credulously cited a debunked, extremely low quality study claiming that Republicans died more because fewer of them were vaccinated for COVID (though a majority were), but they didn’t cite the debunking or notice themselves that the study was invalid (a fuller treatment by Prasad here). In 2024, they still relied on notorious leftist misinformation, such as the sudden, arbitrary belief that masks — even cloth masks — reduce COVID infection rates, a belief for which there is no quality evidence and which the only published randomized trial did not support, and social distancing, which Anthony Fauci had already conceded had no scientific support. The paper is full of arbitrary authoritarian leftist beliefs and superstitions, which they treat as though they were simple, established facts, not unsupported, contested, or debunked.

Perhaps the most notorious leftist policy misstep was prolonged school closures, which were extremely harmful to children, especially in learning loss, yet Van Bavel and Knowles don’t mention it. It’s a vivid example of polarization because leftist teachers lobbies reversed their position on reopening schools in Fall, 2020 simply because President Trump called for it (Levinson, Geller, and Allen, 2021 — see section 3). Such a severe, reactive partisan mentality is disturbing when it impacts children, learning, and health (there were many health impacts of school closures). And leftist ideology was front and center in posts like this from the Chicago teachers union:

Explaining the cult ideology expressed in this tweet is out of scope here, but you can see how freely leftists assert the motives of strangers who take mundane policy positions and of parents who wanted their kids back in school. (Contemplate the fact that these extremists are teaching children.)

Yet Van Bavel and Knowles exclude all traces of how the left behaved during COVID — the scientific illiteracy, reactive partisanship, authoritarianism, and dogmatic hatred and dehumanization of people who declined an experimental pharmaceutical product that wouldn’t tangibly help most people, etc.

Nor do they cite the many harms of the lockdowns, the harms of COVID shots, especially to young people in relation to the faint benefits, or how so many Americans lost their jobs for declining the shots (most of whom had already had COVID). That’s a rather severe harm to innocent people, as is denying transplants to children who didn’t get the shots. COVID isn’t relevant to most people in terms of mortality or hospitalization risk, the shots don’t prevent infection or transmission (many leftists still seem unaware of this fact, even as they keep getting COVID — it’s a confusing situation), and there’s no apparent case for giving them to children — it’s not common around the world and I’m not aware of any published cost-benefit analyses, which would be difficult given the lack of randomized trials and the subjectivity of the weights assigned to the unknown long-term risks of new pharma. Yet even children are denied organ transplants over an irrelevant pharmaceutical product. Leftists’ obsession with forcing people to get COVID shots is worth researching from psychological and science literacy standpoints (Do they know that they don’t prevent infection or transmission? That non-elderly healthy people aren’t at risk from COVID?, etc.), as are any harms ensuing from said obsession.

Finally, it’s awkward to discuss politics and health without addressing what is arguably the worst medical scandal in history — the mutilation and sterilization of thousands of children on new leftist “trans” ideology grounds. In America, the sterilization started in 2007 at Boston Children’s Hospital (Biggs, 2022), and mutilating surgeries started by 2012 at the latest. Indoctrination in leftist trans ideology is widespread in schools nationwide, even in elementary, and it seems to have started in the last decade (examples here). Over 6,000 children age 12+ have already been mutilated, mostly girls whose healthy breasts were cut off, with a few hundred genital mutilations (castration, penectomy, vaginectomy, vaginoplasty, etc.; all published estimates summarized here). Thousands have likely been sterilized by wrong-sex hormones, usually given after puberty blockers, which are implanted in children as young as eight (tens of thousands of children have been given blockers and/or hormones — the sterilization rate is unknown, and varies by sex and other factors). For some children, blockers permanently destroy their ability to orgasm before they’ve ever experienced it. The victims are disproportionately autistic and in poor mental health.

As of 2025, leftist-controlled states continue to sterilize and mutilate children, while most conservative states have banned the practice, joining Britain, Finland, Sweden, and Norway (this is only happening in the West, as it’s a Western leftist ideology). This scandal snuck up on us, is difficult to comprehend, and hard to communicate to the unfamiliar without meeting disbelief. For unclear reasons, American leftists/liberals suddenly acquired metaphysical beliefs in “trans children”, extending the academic left’s “gender identity” ideology and constructs. We face a situation where seemingly millions of Americans believe that a boy can actually be a girl, and vice versa, though it’s not clear what that means. There’s near zero communication between leftists and non-leftists/dissident leftists on this topic, nor any research into this cult psychology. They apparently believe that a child can be “born in the wrong body”. We have countless parents — usually mothers — asserting that they knew their son (who they call their daughter) was “trans” at age four or even two because of trivial behaviors like preferring the color pink or putting on a dress. The children look like hostages in many of the videos: here are several illuminating clips, including a detransitioner. Be warned — they’re disturbing. There’s no research, so we’re limited to direct engagement, which is perhaps fortunate. This scandal is obscured by Orwellian doublespeak like “gender affirming care” and wildly false claims that the mutilation and sterilization are lifesaving.

Sterilizing a child, making children lifelong medical patients, cutting off their healthy breasts, castrating them, etc. is all extremely harmful to health and violates our basic principles of consent and human rights. It’s unconscionable to exclude this issue when purporting to survey the impact of politics on health in the United States.

Back to the false claims Van Bavel and Knowles made — let’s dig into the idea that the number of gun laws, or “gun control” generally, could increase life expectancy, and look the Montez, et al. study.

Gun control

The idea that the number of state gun control laws – the number – would account for longer life expectancy seems mystical. It ignores where guns land on causes of death, it’s a mindless, contentless variable, and in the hands of partisan social scientists extremely vulnerable to manipulation. First, how would any state gun control laws increase life expectancy? What sorts of laws are we talking about? By what mechanisms would they increase life expectancy? Why wouldn’t gun control laws reduce life expectancy?

A core problem is that there isn’t much variance in America in terms of the ability of most people to own firearms. The right to keep and bear arms is formally recognized in the US Constitution. In every state, if you’re an adult and don’t have a criminal record or severe mental health history, you can go buy a firearm. (I don’t know about DC.)

Firearms are extremely helpful in stopping crimes, usually without having to fire them (see English, 2021). We might expect broad firearms ownership to increase life expectancy by reducing the murder rate by preventing murders, but murder is extremely rare, clustered in criminal populations, accounts for <1% of all deaths, and some of the defensive uses of firearms will kill the murderer (like the story on top of this article about the pregnant woman with the AR-15) — that would be a wash on life expectancy estimates. Only a third of the population reports owning firearms, that share has only slightly increased over time, and most gun owners don’t carry their weapons, so effects are hard to detect. (However see Moody and Lott (2024) for evidence that right-to-carry laws reduce the murder rate and Constitutional carry1 laws reduce rapes, though I’m not endorsing their study without spending more time with it, and such effects won’t matter for life expectancy estimates.) Firearms are most helpful in stopping the more common crimes of robbery, burglary, rape, assault, etc. — you’re very unlikely to face an attempt to murder you if you’re not a drug dealer or similar.

What gun control laws do states have that would plausibly increase life expectancy? I’m not sure. To increase life expectancy, they’d have to reduce the murder rate. Not the gun homicide rate — firearms can be substituted, so we have to focus on the murder rate to avoid missing a shift in methods. A 5% reduction would be <0.05% of deaths. Key is how a gun control law, e.g. a magazine capacity limit, would cause that reduction.

Suicides can be ignored for our purposes since state-level gun control doesn’t change the reality that anyone without a record can go buy a handgun, in all states, and alternative methods are easy. A magazine capacity limit or a ban on AR-style rifles isn’t relevant to the ability to take one’s own life with a firearm. Suicides are <2% of deaths annually, so if we reduced suicides by 5%, that’s <0.1% of deaths (though the rates vary by state). There are also ethical problems in assuming that all suicides should be prevented — these are people, lives, and contexts we know nothing about. The ethical issues wouldn’t impact life expectancy analyses, but they undermine the remarkable premise that we should engage in mass coercion to prevent some number of suicides we know nothing about.

(Accidental gun deaths are the most rare type at a few hundred per year, about 0.001% of deaths, so no dice.)

What did Montez, et al. do? It’s not clear. This is the uncited study that Knowles ultimately fell back on. Montez, et al. said they looked at nine gun control laws from 1970 to 2014, and gave a binary 0 or 1 score to states for each law, where 1 marked the presence of the leftist law, and 0 marked a conservative policy/the absence of the law. They said “a state's annual score for each policy domain was calculated as the sum of the liberal policy scores minus the sum of the conservative policy scores”, but this makes no sense since the conservative scores are zero — subtracting them does nothing. Note also that this is not the count of gun laws that Montez cited in her essay/table, which Van Bavel and Knowles cited — that was from Siegel, et al. (2017). This is not a count.

The nine categories under Gun Control:

The core method is invalid, and there are several problems with this list:

1. The Brady law is a federal law mandating background checks, which took effect in 1998. Background checks are a nationwide reality — it’s not clear what state variance one could capture, or how it could plausibly increase life expectancy.

2. The federal government banned “assault weapons”2 from 1994 to 2004. It’s not clear what state variance one could capture in that span, or how it could plausibly increase life expectancy. (Fewer than 5% of gun homicides, which are a subset of all homicides, were with rifles of any type.)

3. It’s not clear what “background checks (dealers)” and “(private)” refer to, or how they could plausibly increase life expectancy. Gun buyers are subjected to background checks at dealers in all states — that’s the Brady law.

4. All dealers are required to have a Federal Firearms License (FFL). It’s not clear what “dealer license required” refers to, what state variance one could capture, or how it could plausibly increase life expectancy.

Note that each of these categories was given a binary coding of 0 or 1. The authors offer no explanation for any of them — there’s literally no explanation in the paper — and they don’t provide their data, codings, etc. We can’t see anything.

Apparently, we’re just supposed to believe that “gun control” below means something, without knowing what it means.

The authors don’t fully describe their models — only Model 1 is even mentioned in the paper, and these tables are hidden in the Supplemental Materials. At least one of the fuller models controls for unemployment rate, but it’s not clear which. There’s no significant effect for men with the fuller models, and it starts to go negative.

Note that they didn’t control for race, which makes this useless, though this research is already invalid.

Recall that Knowles claims the above unspecified study and irrelevant results support this statement:

People who live in states with more liberal social policies, such as generous Medicaid coverage, higher taxes on cigarettes, more economic support (for example, minimum wages) and more firearm regulations live longer than their counterparts in states that embrace more conservative policies.

There’s no model for people as such (just men and women separately), there’s no count of gun control laws, and cigarette taxes is the only variable that exists here. Minimum wage (apparently uncorrected for cost of living) is buried in “Labor (private sector)” with eight other variables. “Generous Medicaid coverage” is either buried in “Health and welfare” as “Medicaid expansion” with 14 other variables, or it’s not here at all, depending on what they meant.

We have no idea what the correlations are between those variables and life expectancy — for men, women, or people as such. Van Bavel and Knowles have no support for their claims, even if we believed the Montez, et al. analysis, which we definitely should not.

Typing “gun control” is easy

I too can type “gun control” and put some numbers next to it.

Gun control: 2.358, p < .01

Voila! Science!

That’s not anything. As we’ve seen, we don’t know what Montez, et al. did with their gun control variable. We don’t know what their categories mean, why they match federal laws, or the scores they assigned states. But there’s a deeper issue — their method is invalid on its face and should never have been published.

Why would we allow partisans to make a list of policies? We can make lots of lists of nine or five or fifteen gun control laws — what makes their list valid? This method is completely arbitrary — in the selection, the number, and the math.

This method also gives equal weight to each policy. Why? Why should open carry count as much as whatever “background checks (dealers)” means? States that allow open carry allow you to visibly carry a firearm, typically holstered, as opposed to concealed carry. Only four states currently ban open carry, so there’s not much variance, and it’s an extremely rare behavior (I’ve only seen one person openly carry in Arizona). It’s not relevant to crime since criminals don’t tend to walk around with visible holstered weapons.

If you’re familiar with the gun control debate in America, you’ll notice that Montez, et al. excluded the two most prominent issues from their list — concealed carry and magazine limits. Why were they excluded? I have no idea, but the method is fundamentally invalid — it wouldn’t be made valid by adding missing issues. Note that adding issues would arbitrarily change the math and the results.

The core issue is that academics in the social sciences are allowed to invent arbitrary, often undisclosed methods. This is very unlike the physical and life sciences, where the methods are established, validated, and familiar to reviewers. In social science, it’s common for reviewers to never have seen the method before. They don’t think deeply about them, and allow researchers to just claim that they measured “gun control”, “misinformation”, or “racism” (example item for “racism”: “It is easy to understand the anger of black people in America” — disagreement or even neutrality scores you as “racist”). Reviewers are dazzled by complex inferential statistics that they won’t validate or even understand, and they don’t look at the data — no one looks at the data, even to see that it exists. As usual, no data was supplied with the Montez, et al. study — we should stop talking about research for which there is no data, and all such research should be retracted by journals and removed from the literature.

The methodological flexibility of social science means that there are hundreds of ways we could measure something as broad as “gun control”, multiplied by many different choices we can make in our inferential statistical modeling. Journals will publish anything if it advances leftist narratives and power, especially if it makes conservatives look bad.

For example, we could’ve chosen a different, longer list of laws, and broken them into four groups. For the first group, we’d assign states scores from 0-6. For the second, we’d assign scores of 0-3. For the third, we’d assign scores of 0-1.5. And for the fourth, we’d assign scores of 0-1.

Then we’d sum the scores and correlate that sum score with “gun deaths” (mostly suicides), controlling for nothing at all — just a simple Pearson correlation. And note that a “gun deaths” variable is nifty because it can easily mislead readers by hiding increased or flat murder and suicide rates.

To make it really tricky, we could violate spacetime by correlating gun laws as of January 21, 2025 with gun deaths from 2023, as if gun laws passed in 2024 could have saved lives the year before…

I just described the method used by Everytown, a leftist gun control lobby founded by Michael Bloomberg. Their data and ratings/methods are secret beyond the description I gave above. (They do tell you what laws are in each group, but they don’t explain their scoring process beyond that, e.g. what sort of legal reality would get a state a 0, 1, 2, 3, 4, 5, or 6 on the Stand Your Ground law category, or any other category.)

Montez, et al. did something that they didn’t describe, and it turns out they got impossible results as far as gun control laws actually causing an increase in life expectancy.

Life expectancy

Life expectancy has been going up everywhere for over a century because of the industrial revolution and capitalism, and the resulting gains in standards of living and advances in technologies relevant to survival, like sanitation, transportation, logistics, affordable oil/gas for heated homes, health care, antibiotics, etc. (Vaccines have saved millions of lives, but came after the major gains in life expectancy, mostly in the 1960s and 70s.)

Montez, et al. plugged their lists of leftist policies — converted into invalid composite scores — into the trend you see above.

The problem is that lawmaking increased dramatically in the 1970-2014 period they chose, in all states — mostly leftist-oriented laws. It’s not clear how plugging in anyone’s lists of laws is valid unless you control for all the obvious things, like standards of living, race, and well, all the things we know predict life expectancy.

We could test other lists of laws, but I’m not sure there’s any valid list-of-laws method, especially if the obvious predictors of life expectancy explain away all the variance.

Moreover, we can separately invalidate their claims with simple reality check math.

Life expectancy is calculated with life tables. Like this one I got from the CDC for 2010 (randomly chosen year relevant to Montez, et al.).

For now I’ll just point you to the highlighted cell. That person-years figure is cumulative — it’s the sum of all the values in the previous column: the annual person-years [L(x)].

Life expectancy is that total person years figure divided by 100,000 — the numbers are all out of 100,000, which is why the first cell under survivors is 100,000.

It’s 78.7 for people born in 2010.

Okay, so if state gun control laws reduced the murder rate in a state by 5%, that’s less than a 0.05% reduction in deaths if the state’s death and murder rates are similar to the nation’s.

A 0.05% reduction in deaths is much less than a 0.05% increase in life expectancy, since the death rate is only ≈1%. Right now, I don’t know how to do the correct math on that life table, in part because the probability of being murdered will vary by age (and it should be controlled for race anyway). I’m curious enough about this that I’ll probably learn and come back to it.

Recall that Montez’s only significant effect of their mysterious gun control variable was on women’s life expectancy. That presents another logical/math problem — women are killed by guns at much lower rates than men.

This is firearm-related homicide deaths for 2021, per the CDC. As you can see, women are about a fifth as likely to be killed this way, so moving around their life expectancy is even more implausible given the math above.

How about suicide?

These are suicide rates by sex and method, for 2000-2018, per the CDC.

Again, there’s no plausible mechanism by which state gun control laws — open carry, magazine limits, etc — could reduce suicide rates, but the basic suicide data makes the idea even more tenuous. Reducing a 0.006% death rate (6 per 100,000) by even 10% is not going to show up in life expectancy estimates, and this combined with a similar effect on gun homicides simply cannot increase life expectancy by a half year, as Montez, et al. reported:

So even if these laws — which they didn’t explain and which duplicate federal laws — could somehow reduce murder and suicide rates, they could not have had the reported effects on life expectancy.

Often, basic logic and exogenous real-world data debunks academic research. This is underexposed because no one is systematically evaluating academic research. Our civilization is still stuck in an unfortunate place where we don’t understand that journals will publish anything, much of it false, and that academic researchers can be trivially debunked by outsiders doing basic fact checking3 and reasoning.

A good way to capture the invalidity of a lot of academic research is to ask yourself how you’d approach a question.

Q: Do state gun control laws increase life expectancy?

You’d probably want to throw out the question because of the reasons noted above, but whatever you came up with, you wouldn’t make that list of nine leftist gun control laws, generate a composite score from them by any method, much less theirs, do the same for a bunch of other partisan leftist topics, and regress life expectancy, which is on a 150-year upward trend, on such variables while not controlling for race, standard of living, burden of disease, etc.

I recommend thinking about how you’d answer a question or test a hypothesis before you read journal or news articles about them. You can do this from the headlines. Maybe write down a summary of what you’d do, then read the article. It’s fun, and you’ll be shocked by the invalidity and stupidity of the methods that academics used instead. Prometo.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

On October 29 of last year, Jonathan Lemire at Politico doctored a Biden quote.

I’ve never seen anything like it. Biden infamously called Trump supporters garbage:

The only garbage I see floating out there is his supporters. His — his demonization of Latinos is unconscionable, and it’s un-American. It’s totally contrary to everything we’ve done, everything we’ve been.

He was talking about a comedian at a Trump rally joking that Puerto Rico was a “floating island of garbage”, playing off its widely reported garbage crisis.

Lemire published a story about 20 minutes after Biden’s comments, which Vice President JD Vance caught later that night (link):

See what Lemire did? He stripped the quote down to the first three words, filled in his own wording, and added “hatred” as part of the quote.

Biden never used the word “hatred”. Lemire added it, and wrapped it in quotes (!) He doctored the quote. He literally fabricated a quote.

That’s plain journalistic fraud.

Yet the media has been silent on this, as has virtually everyone else. Even Vance didn’t make it explicit that Lemire doctored, fabricated the quote. He called it a falsehood, but it’s worse than that – he fabricated, inserting a word and wrapping quotes around it.

Donald Trump, Jr. is the only person I’ve seen who noticed that Lemire lied, calling him a liar in this post.

Three days later, Lemire announced that he was leaving Politico, and signing a deal with MSNBC. He was presumably fired for his fraud, yet none of the news coverage of his move disclosed that he had committed fraud three days earlier. The Hill doesn’t mention it. It’s the strangest, most suppressed case of journalistic fraud I’ve ever seen. No one is talking about it. Could Politico even have kept him? Would anyone call it out? It’s truly remarkable that someone can commit fraud, literally fabricating a quote, and there’s no coverage or discussion at all. And there are no questions for MSNBC. After all, they employ a fraud.

Moreover, notice Lemire’s tactic of microquoting, common in leftist media. Often they’ll only quote a single word or three, and fill in the rest with paraphrasing. This obviously isn’t ethical journalism — it’s not possible to know what someone said when journalists do this. Lemire took a 30-word quote, and stripped it to three words, and added his fabricated word.

(The New York Post has a good rundown of the sequence of events as leftist media scrambled to cover up Biden’s statement that night.)

“The transcript says…”

This story also illustrated a chilling behavior in leftist media. In story after story at Politico, CNN, NBC, ABC, CBS, the New York Times, the Washington Post, and other leftist outlets, reporters cited only the White House transcript of Biden’s statement, as though we’d care about a transcript when we have the 16-second video.

Transcripts aren’t real. They’re just someone’s account of what someone said. We don’t need them if we can just hear or watch the person say it. Leftist media generally withheld the video and cited the WH transcript instead.

Predictably, the transcript was, like Politico’s story, doctored. Biden staffers added an apostrophe to “supporter’s”, trying to say that Biden was only referring to the comedian (who is never identified as a comedian by the WH or most media — always a “supporter”). The doctored version makes no sense since no object follows the possessive. Here’s the video:

If we’re to stay connected to reality, we cannot substitute Orwellian transcripts for it. The reality here is the video — no one’s transcripts matter, certainly not the White House’s. Leftist media is systematically trying to wedge itself between the public and reality, distorting it to advance leftist power. Citing doctored government transcripts while suppressing the video is a chilling example of how Orwellian and unethical American media has become.

Politico

Even if they fired Lemire, Politico remains a leftist outlet that publishes a high rate of false claims, what leftists call “misinformation”. They’re extremely unreliable, which is why Wikipedia asserts that they’re reliable. (Wikipedia asserts that virtually all leftist media outlets, and zero non-leftist outlets1, are reliable. They have no data to back their assertions, commissioned no research.)

For example, Politico still reports the doctored WH quote with the apostrophe, uncorrected — Lemire published this article two hours after his original fraud, which was deleted.

Three days later, a different Politico staffer reported the scandal of the WH doctoring the transcript, but Politico hasn’t corrected articles like Lemire’s that include the WH-doctored quote.

Note that Politico makes finding their original, false articles difficult by appending random 8-digit numbers to their URLs, creating lots of different URLs for the same article, which makes it hard for web archives to archive them. For example, the current version of Lemire’s story has a URL that ends with: …biden-sparks-garbage-firestorm-00186187.

A fresh example of a wildly false claim by Politico is in this story on Trump’s Executive Order banning federal funding of child mutilation and sterilization:

Gender-affirming care for minors and doctors’ ability to provide it has been widely accepted by major medical institutions, including the American Academy of Pediatrics, through independent reviews.

There are no independent reviews that find good evidence for the benefits of “gender-affirming care”, and these doctors lobbies have not conducted or even cited any. Zero. Not a single one. Every systematic review around the world has found poor or no evidence to support giving children sterilizing hormones, puberty blockers, or mutilating surgeries — in Britain, Finland, Sweden, and Canada that I know of.

None of the leftist doctors lobbies in America have conducted a systematic review (which wouldn’t be independent), though the American Academy of Pediatrics briefly suggested they would. No one in America has. There’s literally nothing. Moreover, these leftist doctors lobbies could not conduct such a review at this point — their ideological bias is too strong, their financial conflict of interest too obvious, and their legal liability looms as more children sue them. (To get a sense of the numbers of children harmed, see my snapshot report.)

So Politico’s claim is simply false. These leftist lobbies have not cited any independent reviews, and no such reviews exist that support sterilizing or mutilating children. I notified the author Daniel Payne this morning. I think the odds of a correction are low, since Politico only hires leftist activists and don’t generally correct false claims. Politico is littered with falsehoods like this — I found this one in seconds by searching “gender affirming care” and it was the first and only article I opened. We could find a dozen in ten minutes.

Fin

Jonathan Lemire is a fraud, and should not be employed in journalism. If MSNBC retains him, MSNBC should be properly characterized as a fraud-employing outlet, in addition to being extremely unreliable and mentally disordered. (Note that Wikipedia’s activists also assert that MSNBC is a Reliable Source (!) We simply must build an alternative, and soon.)

Conservatives, libertarians, and high-integrity leftists have so far failed to commission the systematic research we need to establish the reliability of various media outlets, along with various forms of bias that don’t manifest as simple false claims. We need rigorous, comprehensive research that assesses false claim rates and multiple forms of bias at all major outlets. It’s unacceptable that we don’t have this data. As far as I know, no one is even attempting it.

With such data, all open and explorable online, we could properly characterize outlets like Politico, MSNBC, NYT, et al. Conservatives and Republicans in particular are being gashed daily by narratives that they “don’t believe in facts”, that Trump lies constantly and so on when the reality is more the opposite, and these outlets are themselves extremely unreliable, even to the point of fraud. There’s no need to just passively take it, and let millions of Americans internalize these false narratives and walk around with countless false beliefs. I’m confident that any major conservative outlet is significantly more reliable than Politico, WaPo, etc. — if that’s true, it matters, and it needs to be established with unimpeachable data and methods. Let’s going on this. Contact me below and we can get rolling.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

Last October, Amazon and Blue Origin founder and Washington Post owner Jeff Bezos published this essay explaining his decision to not have the Post endorse a Presidential candidate. He noted that the Post and the wider media is widely perceived as politically biased, but seemed to think this was merely a perception problem and that the Post is fundamentally credible. He contrasted them with “unresearched podcasts” and “unverified news sources”, as though the Post is a reliable (and “verified”?) news source that powers its reporting with research that podcasters and new media don’t or cannot do.

I’m confident that the Post is extremely unreliable — much less reliable than a conservative outlet like the Daily Wire or the indy Free Press — and that its stark leftist bias yields many straightforwardly false claims, along with severe biases in selecting, framing, and writing news articles. (I’d bet thousands of dollars on it being less reliable than those other outlets, but it would take funding to do the rigorous research.)

There’s no better example than the Post’s “fact checker” Glenn Kessler. Since fact checking should be the cleanest and most disciplined of its journalistic operations, the fact that the Post pays for and publishes artifacts like the example below is an indictment of its overall commitment to and ability to produce journalism. It’s confusing that Mr. Bezos hasn’t noticed the frequent false claims in his paper, or the frequent false fact checks. Had he read this one, I’m sure he would’ve been stunned and horrified. Here we go…

In 2017, Kessler decided to fact check Trump’s claim about America’s missile defense system, the Ground-Based Interceptor (GBI): “We have missiles that can knock out a missile in the air 97 percent of the time, and if you send two of them, it’s going to get knocked down.” (Link)

The context was heightened tensions with North Korea.

Kessler also found Congressional testimony where a general gave a “high-90s” estimate for the system’s intercept probability.

Background: The GBI is designed to intercept incoming nuclear warheads in space, before they re-enter the atmosphere. The US has deployed at least 44 of them in Alaska and California, and they’re meant to defend against smaller nuclear powers like North Korea and accidental/rogue launches. (Russia and China have too many missiles and warheads to intercept.)

Kessler said: “Several experts said the high-90s claim appears to be based on faulty math.”

There is no math – not anything public he would know about. His math:

He looked at the program’s 18-year test launch history and… divided the number of successful interceptor launch tests by the total number of tests.

He thought this gave him the intercept probability of the currently deployed system.

He also thought this is what the Air Force did – their “faulty math”.

The Air Force didn’t divide successes by total tests to get its estimates. Those tests weren’t commensurable – they weren’t the same missile from test to test, and the tests themselves were different in design and goals. Missile and rocket testing is iterative – they learn from each test, especially failures, and make changes to the missile and software as appropriate for the next test. This testing spanned 18 years – the vehicle they first launched in 1999 had little in common with the 2017 version, and used a surrogate booster. A 2002 test failed because the kill vehicle didn’t separate, and in 2005 the arms holding the missile in the silo didn’t retract, so the missile didn’t launch — those resolved failures have nothing to do with the intercept probability of the system deployed a decade later.

The USAF developed at least three different publicly known versions of the GBI. You can’t divide anything by anything here to get the intercept probability of the deployed system. You might as well divide the missile’s height by its mass times π. There’s nothing you can do with a calculator here.

The intercept probability of those missiles is determined by a classified methodology that draws from technical test data and various engineering domains, as well as intelligence assessments of adversary ICBMs, including their expected trajectories, maneuverability, and decoy characteristics. All of that is classified. The interceptors are one piece of a multifaceted system including large ground-based radars, launch detection and tracking satellites, guidance uplinks, etc. Moreover, all those factors are dynamic, so intercept probability can change given a change in any of the components or adversary systems. It’s not possible to fact check a President’s claims here (and stunningly reckless to do so in the middle of a showdown with a volatile nuclear power).

Incredibly, it gets worse. Kessler’s mindless division gave him a 56% estimate (10 successes out of 18 tests).

56% is not 97%, but he thought he could get to 97% with extra steps, so he stipulated four interceptors launched against one incoming nuke. (Remember, 56% is a completely meaningless number here.)

The equation given the independent probabilities is 1 − (1 − 0.56)⁴. That doesn’t work for Kessler because it yields 96.3%, not 97%. So then he concluded:

“MDA appears to be rounding up to 60 percent.”

He means that he thinks that the US Air Force Missile Defense Agency took the 56% successful test rate from his division and rounded up. He genuinely believes they did his division thing, and that they would round up to the nearest multiple of 10% in computing an intercept probability.

With that assumption of insane, certainly criminal rounding, he lands on 97%. 1 − (1 − 0.6)⁴ = 97.4%

This is amazing. He thinks this is where Trump’s number came from, and he proceeds to pick it apart with his leftist NGO “experts”, who, incredibly, also thought this arithmetic was what the USAF actually did.

This artifact belongs in a museum. Just the odds that Kessler could land on 97% with his abuse of a calculator are so low – it’s an incredible coincidence and a candidate for the funniest fact check of all time.

Kessler gave Trump “Four Pinocchios” – the max – and this fact check is in the Post’s repository of thousands of purported false or misleading claims by Trump (and probably in Wikipedia’s wildly false article on false claims by Trump). That repository is full of false fact checks. (Mr. Bezos, when I say false, I mean straightforwardly false. Many others are misleading, and some are invalid, but many are simply false.)

This fact check was invalid — an arbitrary, painfully stupid artifact — and also false in saying that Trump made a false claim (and that he lied, using the unprofessional Pinocchios rating system that calls people liars without evidence). Kessler had no basis for it — he didn’t falsify the claim, and it’s probably not possible to falsify it short of a whistleblower investigation uncovering fraud in the tests or estimates.

Root causes

I see four root causes here:

1. Kessler doesn’t know anything about rocket/missile testing, which led him to make an embarrassing mistake. (It’s common for leftist “fact checkers” and “misinformation researchers” to adjudicate on topics they know nothing about, even deigning to fact check peer-reviewed scientific journal articles.)

2. Kessler is a partisan leftist and is extremely motivated to claim that Trump lied about something. (We can show how he lets leftist politicians get away with stunning lies — actual lies in that we can be confident they know what they’re saying is false.)

3. Kessler often only cites other leftists as “experts”, many of whom are not experts. This is common with leftist “fact checkers”. I call the method LQL: Leftists Quoting Leftists. His experts here were stunningly incompetent. (There’s a lot to say about the authoritarian, anti-reason epistemology of the left, and their new dogma around “experts” who seem to mostly make false claims, but that’s for another day.)

4. No one at the Post noticed, or noticed his many other false fact checks. There’s no active intelligence at the Post of the sort that can see basic, severe problems like this. His fact check of the Biden-Trump debate last year is another example where it’s obvious that no one is attentively reading his work and checking his sources — if they are, they’re not able to reason well or grasp basic descriptive statistics.

Mr. Bezos, you’re obviously familiar with rocket testing. You know how insane this fact check was. The Post isn’t operating at a standard of research and fact checking that would compare favorably to Joe Rogan, much less a more focused, expert podcast or writer. Moreover, Rogan tends to correct his false claims when he learns they were false. The Post does not. I emailed Kessler in July, 2022 about this one — no response, no correction. I’ve tagged him at least four times on X on it over the last couple of years — no response, no correction. This is incredible. And again, there are many, many other false fact checks, false claims in news articles1, and lots of interesting forms of invalidity and deception2.

Joe Duarte grew up in small copper mining towns in Southern Arizona, earned his PhD in social psychology, and focuses on political bias in media and academic research. You can contact him at gravity at protonmail.com.

Afterword

With a team and an LLM, we could cleanly and unassailably assess the Post’s repository, and similar ones at NYT and elsewhere, using a rigorous epistemic framework and solving the domain knowledge problems. I’m confident that most of the fact checks unfavorable to Trump are themselves false or invalid, and that Trump’s actual false claim count is much lower than widely assumed. More importantly, there’s a large corpus of false fact checks and claims in news articles that go well beyond Trump — this is a systemic problem with America’s information environment, it was created by the left, and it needs fixing.

I’m writing a project proposal to systematically assess the reliability of media outlets, including self-styled “fact checking” groups, as well as various forms of bias. No one has done this — all current efforts that might sound like this are scams like NewsGuard. No one has actually rolled up their sleeves and systematically, validly assessed news headlines, articles, and shows for false claims, and any leftist group or academics that attempted to do so would end up ignoring most false claims by leftists, while mirroring Kessler’s record in false assessments of falsity by conservatives, Republicans, etc. For example, see the fraudulent and false (separate issues) “misinformation” research by Rand, et al. here.

So, Mr. Bezos if you want to have a huge impact on media reliability and credibility in the United States, supporting my project or something like it would be the best way I can think of. It’s long overdue — a country of America’s size, wealth, and role should have several redundant, rigorous, thorough assessments of media reliability, and we have nothing.