The establishment's theory of race differences in socially valued metrics is that this is due to "systemic racism", a kind of Marxist conspiracy theory where the dominant group (Whites) keeps other peoples down.
There are clear testable predictions from this theory. In places where racist, White people have more power, outcomes for non-Whites, especially Blacks and Hispanics should be worse. Recall that the US demographics by county look like this.
Of course, Republicans are racist in this theory.
Thus, the theory predicts that in these areas of the USA, Blacks and Hispanics should be particularly worse off compared to Whites. But the exact opposite is actually true. The race gaps are smaller, not larger, in Whiter and more Republican areas.
The above figures are for test score gaps, but the same holds true if we look at social status gaps. Here's some maps of race gaps in social status.
So we need another way to explain the variation in social status gaps. Well, it's easy. Test scores -- academic achievement that mainly reflecting intelligence -- explain why race gaps are smaller and larger in various locations. Meritocracy works.
It gets even worse for the theory. It turns out the effect of White population share and Republican vote share are interactive. The areas with the smallest race gaps are the ones with the largest White populations and the largest Republican vote shares combined!
There we have it. The Marxist conspiracy theory that is the go-to explanation of race relations fails when we look at county-level variation across the United States. The predictions it makes are exactly opposite of reality. If anything, it seems Republican Whites are good for minorities.
If you want more details, read my new blog post:
New study out: Systemic Racism Does Not Explain Variation in Race Gaps on Cognitive Tests
"Once dull, always dull". Special education classes have very good metrics for teacher education and students per teacher, but they don't seem to improve much from this extra effort. So Terman infers from this that these factors cannot be of great importance.
New study of 2700 Indian whole genomes shows that Indo-European/Yamnaya/Steppe is quite low, about 15%. It is a bit higher in the north (almost 20%) and among those who speak IE languages, but not impressively so. Too much mixing since the arrival.
Inbreeding is strong. The median person had identical blocks in their genome at the level suggesting their parents were 3rd cousins, whereas the human average is about close to 4th cousins (Africans less).
Annoyingly, the data is not public, even though American taxpayers paid for this. Can't wait for Trump admin to maybe do something about this abuse (inb4 fell for it again award).
Not everybody commit scientific fraud at the same rate. Let's look at some data. First retraction rates.
We can also look at the top list of most fraudulent researchers ever (so far those that were caught). Note: data from 2019 list. There's quite a few non-Europeans.
This is important because we want a per capita measure of sorts. For highly regarded journals, European built countries produced about 75% of science (Nature Index), but maybe 30% of top fraudsters.
You have maybe seen this plot. It shows ratings of races in USA by race. The key result is that each race favors their own, except for Whites who are apparently race-blind.
Since it only covered a single year, 2020, I wondered if this would replicate across years of data. So I downloaded the data for 1964-2024.
So we see that the result replicated (I didn't use survey weights). And indeed, in 2020 and 2024, Whites have about zero ethnocentrism.
But this pattern among Whites is more complex. Here are the ratings by political ideology (self-rated 1-7 scale). The below average attitude towards fellow Whites is concentrated on the left, just as @ZachG932 found before. Especially the far left.
Mental issues roughly follow a hierarchical pattern, like cognitive abilities, like a general factor on top. At least, statistically.
The motivating factors behind this approach compared to the categorical (diagnostic) approach are: 1) evidence of continuity between clusters, 2) binary encoding of continuous data loses information, 3) correlations among diagnoses are the norm, 4) a given person may not quality for any particular diagnosis, yet have severe symptoms.
Some aspects of mental problems haven't been integrated into the hierarchical model yet. Say, unusual sexual interests (from foot fetishes to pedophilia and rape fetishes).