Anti-racism trainings probably lead people to accuse others of racism even when they're not racist.
That's exactly the result of a new study on DEI trainings, with a special focus on the impacts of the works of Ibram X. Kendi and Robin DiAngelo.
Let's dig in🧵
In the first experiment, the researchers took 324 participants and randomized them to either read an Ibram X. Kendi or Robin DiAngelo excerpt or to a racially-neutral condition where they read about corn.
Here are some excerpts from the reading materials, for your understanding:
After learning, for example, that western countries are compromised by virtue of their racist ideologies and pasts, participants were presented with a scenario that was totally racially neutral.
The scenario is described as follows, and everyone involved did nothing racist:
The participants who were exposed to the 'racism' scenario imagined more racism into existence.
They believed there was a lot more bias, tons of microaggressions and whatnot, even though there was nothing.
What's worse, the participants who read the DEI passages also wanted to punish the "offenders" who—I'll remind—literally did nothing racially biased.
They were more likely to want to harm people who did nothing due to their own imaginations.
These findings were so shocking and forceful that the authors immediately sought to replicate them.
They gathered a nearly three-times larger sample and found... the same results!
But this wasn't the last study. We know that people exposed to DEI racism trainings invent racism out of thin air, but what about other -isms?
Next up is Islamophobia.
The 2,017 participants in this study read either anti-Islamophobia materials or stuff about corn.
After either reading about corn or materials from the Institute for Social Policy and Understanding (ISPU), participants were then asked to evaluate identical trials, for either the clearly-Muslim Ahmed Akhtar or the clearly-just-White George Green.
Participants though the trial of Ahmed was considerably more unfair after they "learned" about Islamophobia.
But once again, there was no bias. They just read the DEI materials and invented the bias in their minds.
But why? Mechanistically, it does not seem that learning about (and seemingly believing in) Islamophobia increased tolerance for Muslims.
What it did was just to increase the perception of bias. Islamophobia materials did not boost positive sentiment towards Muslims:
A final major point of DEI trainings nowadays is caste.
I am referring not to "involuntary caste" stuff a la scholars like Ogbu, but to the Indian caste system.
As the timeline shows, its supposed importance has rapidly gained acknowledgement across the U.S.
Despite institutional acceptance that caste matters, and in particular because of bias against members of low castes, most Americans probably still don't understand caste.
So in this experiment, participants were exposed to caste oppression information, or to neutral caste info:
Participants were then exposed to a totally caste-neutral scenario in which an Indian admissions officer at an elite East Coast university interviews Raj Kumar and, ultimately, Raj gets rejected.
As you might predict from the other results, the nearly 850 respondents who read about casteism invented a lot more caste bias into the scenario than people who read about caste in general.
Not only that, but the people exposed to casteism reading material were more likely to see Hindus as racists and to want to punish the admissions officer.
What was really alarming was that, after the casteism readings, people were considerably more likely to agree with explicitly anti-Brahmin statements that were really rough, like "Brahmins are parasites", "Brahmins are a virus".
These seem like damaging ideas to promote!
Turning back to the original sample, we see something interesting: the people who scored higher on Left-Wing Authoritarianism were more likely to want to punish the people they believed were being racist.
Keep that in mind. Now let's review.
All these large-scale studies, with their simple designs, and direct and conceptual replications, with all of their results, support several conclusions.
First, DEI training introduces narratives that lead people to assume certain groups are oppressors and others are victims.
Second, DEI trainings lead to hostile attribution biases, leading participants to see discrimination when there is none.
DEI trainings ironically promote racial prejudice, hostility, suspicion, and division.
Third, DEI trainings lead to demands for punishment again perceived oppressors, as well as the ideologically impure.
This happens despite the perception of being an oppressor always being wrong in these studies.
Fourth, heightened suspicion of "oppressors" and the "impure" triggers people with authoritarian tendencies to endorse surveillance, purity testing, strict social control, and ever-increasing responses that range from corrective to coercive.
Authoritarians want to punish.
And fifth, the heightened punitive atmosphere generated by DEI trainings feeds into demands for more anti-oppression trainings, creating a self-reinforcing cycle of totally needless suspicion and intolerance.
DEI trainings have been noted to be ineffective at promoting tolerance and productivity, and plenty of people have noticed backfiring.
This adds a new dimension that teaches us about feelings and perceptions of oppression more generally.
With these results in mind, we now know that people are more than willing to totally invent racism and other forms of bias in their heads and to want to harm people because of fully-imagined bias on those people's parts.
The era when everyone was colorblind was better.
Future studies replications with fake groups would be neat, but these probably got close enough using unfamiliar groups and with these large trials due to the nature of them being randomized
0. The sample for the population result is *everyone* with ≥1/16 and ≤15/16 African admixture, the within-family result is for the siblings among them. I didn't plot between-families results, but they're pulled from the same distribution as the population result, so when I have those computed I'll replot, but they shouldn't be any different (will take a few hours, maybe be tomorrow, w/e).
1. Yes, datasets will be merged and sharper results will be obtained for the article on this that's coming out ~shortly. Can't be done for all phenotypes in the UKBB, but can be done for IQ at least. For example, we don't have lipoprotein(a) (lp(a)) measurements in some of the samples of young American kids, some of them don't have objective skin color measurements, etc. As a side note, this result holds up with brain size (not shown) in the UKBB, but I'm unsure if it holds up in the young samples that'll be merged with it, as they're still developing in most cases.
Anyway, the IQ p-value is p = 0.008 (two-tailed) within families and extremely low between them. The within-family result is robust, will get sharper with more data, and is also sharpened by using a latent variable instead of a score, and by correcting for measurement error (not needed with the LVM). Score shown for simplicity and ease of others with UKBB access replicating this. Also, accounting for error in the admixture computation, the p-value would drop a bit further, but not by very much since error is very small.
2. The "IQ per unit of admixture" is statistically indistinguishable between the population and within-family results, and yes, it explains most of the Black-White difference in IQ. I just wanted comparably-scaled results for all the traits here, so you're seeing r's. It's pleasant that the within-family variance reductions aren't enormous for siblings, which is what we expect even with quite high heritabilities given their genetic relatedness. It's the same result we've seen with American data, and it's also nice to see that in the case of this trait, the global admixture result *can* be interpreted like the within-family one. Presumably this only holds with measurement invariance, as we see in the U.K. when comparing Whites and Blacks there. Since we see this in the U.S. too, it's likely that the previous, already-published within-family null—which had a sizable effect in the correct direction which also could not be distinguished from the global r—was just a false-negative.
3. This result replicates with other degrees of relatedness, but we might lose the causal interpretation with those ones because the estimand for, say, a cousin test is different given the identity of the "C" variance component shifts for that comparison.
4. What is architectural sparsity and why is it relevant? Consider this table from nature.com/articles/s4159… (cc: @hsu_steve):
Basically, sparsity refers to the number of variants involved in a trait. It also refers to their effect size distribution. So, for Alzheimer's, for example, the trait is highly polygenic, but APOE explains more variance than the entire rest of the PGS, so while being under highly polygenic control, it remains moderately sparse.
If you're still not grokking what I mean here, consider some distributions of cumulative effects across the chromosomes. Here's the result for lp(a), which is already known to be influenced by essentially one gene. Guess which chromosome it's located on:
Consider, as a comparison, creatinine, which is considerably less sparse, and thus has effects distributed across all chromosomes:
Now, consider what this does to within-family admixture assessments. Skin color, for example, is controlled by only a handful of genes. This means that global admixture shouldn't tag it very much between siblings. The same is even more true for lp(a). And in fact, now we have confirmation of this!
But, we know from other methods that lp(a) is effectively entirely genetically-caused between populations. This is just accepted within the medical community because it is an obvious fact that follows from its strong control by a single gene. You can also figure this out using local ancestry estimates. Basically, the correlation between genome-wide ancestry and ancestry at a causal loci is what we want to get at, and if you know the causal loci, you gain power by restricting your analysis to that area. This is what we find with lp(a) (not shown, but use your brain. The obviousness of this fact is why the author used lp(a) as an example).
Also, in some sense, the frequency of that locus between ancestries gives you what you need without doing all this within-family stuff, if you're confident it's causal. The effect estimate might still be biased by population structure though, so that's worth keeping in mind.
There will be a post soon with more details and the expanded set of results with the additional datasets, robustness tests, and plenty of other fun things to look at.
TL;DR: this is a spoiler, and it shows that, yes, you can explain the Black-White IQ difference in Britain mostly genetically, and the global admixture result that I've posted here before is equivalent to the within-family one. Woohoo for things that should hold up, in fact, holding up!
As a sort of replication of the Young paper, you cannot explain the difference in educational attainment (as years of education) in this way. Why? Well, hard to say. Compensatory factors like I found with the GCSEs? (Haven't read that post yet? Go check it out here: cremieux.xyz/p/explaining-a…). Poor phenotype quality? Very plausible, because education really is a huge garbage heap, but why would that be in the general population and not within families? Maybe it has to do with what other traits admixture tags? Maybe it does replicate, but we just can't see it, because the precision is too poor (possibly the lp(a) story too). Who knows!
Q: Will the combination with more datasets allow us to fix this educational attainment result?
A: No, because most of the other datasets involve young people, not people who have almost all completed their educations, as in the UKBB. That plus the generational change and international incomparability in the definition of educational attainment makes it too poor as a phenotype. Sorry!
Any questions?
Link to a fun previous post from the same dataset, showing a result that *does* hold up within families: x.com/cremieuxrecuei…
Link to another post mentioning the forthcoming article earlier: x.com/cremieuxrecuei…
I want to ping an old post that I've also posted some replications for.
Basically, parents are inequality averse, and they try to compensate for when one sibling is less gifted than another, reducing the ancestry/PGS effect on education within families.
Ever wondered why advertisements heavily feature Black actors when they're just 12-14% of the population?
I might have an explanation:
Black viewers have a strong preference for seeing other Blacks in media, whereas Whites have no racial preferences.
These results are derived from a meta-analysis of 57 pre-2000 and 112 post-2000 effect sizes for Blacks alongside 76 and 87 such effect sizes for Whites.
If you look at them, you'll notice that Whites' initial, slight preference declined and maybe reversed.
It's worth asking if this is explained by publication bias.
It's not!
Neither aggregately (as pictured), nor with results separated by race.
You're on trial, and the jury can't make up their minds. The decision is a coin flip: 50/50, you either get it or you don't.
Your odds of a given verdict depend on the "peers" making up your jury.
If you're Black and they're Black, your odds are good; if you're White, pray.
Though White jurors have, on average, no racial bias, the same can't be said for Black jurors.
Where the White jury gives you approximately the coin flip you deserve, the Black jury's odds for a verdict are like a coin rigged to come up heads 62% of the time.
Once you get to sentencing, things get even worse.
The White jury is still giving you a coin flip on a lighter or a harsher sentence, but the Black jury is giving lenient sentences to Blacks about 70% of the time.
Posts saying Charlie said things he didn't, oftentimes even including videos where he doesn't say what the post says he does, have convinced me.
There will be no organic temperature-lowering coming from the left, because they don't want it.
They believe the things they claim.
They actually believe the people they dislike *are* racists, fascists, homophobes, transphobes, all of it.
They are not reasonable, and they cannot form anything like a reasoned argument for these perceptions.
Even still, it's really what they think.
Accordingly, they will not lower the temperature.
You can't expect people to stop using slurs like "fascist" or "racist" or "sexist"—even if they are completely untrue and it's impossible to provide valid evidence for them!—if they believe they're true.
It shows that the gender wage gap is mostly about married men and their exceptional earnings.
In this thread, I'm going to explain why married men earn so much more than everyone else🧵
The question is:
Does marriage maketh man?
Or
Are all the good men married?
That is, does marriage lead men to earn more, or do men who earn more get married more often?
To answer this question, we have to work through the predictions of different theories.
For example, one of my favorite papers on the subject looked into three different hypotheses to explain the "marriage premium" to wages, and they laid out a few testable predictions: