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What would you consider to be sufficient factual evidence to shift your views on HBD in various directions?
In order for the question to make sense, it is probably helpful to think of one's position on HBD along two axes, as in the Political Compass test, and one discrete parameter that would make less sense on an axis. You're also welcome to point out omitted positions.
x-axis, ranging from 0 to 10, where moving right indicates agreement with the statement: Human populations have significantly different average levels of intelligence, and this becomes far more pronounced in the right tails of the distributions.
y-axis, same range as the x-axis, but measured as 10 * perceived percentage of genetic contribution to the difference above. If you attribute some of the difference to the interaction of genetic and environmental influences, give that half the weight for simplicity.
Parameter z: How does intelligence correlate with the moral worth of a person? This can take on one of a few values:
(-1) Negatively
(0) Not at all
(1) Positively
(i) The moral worth of a person is dependent on their actions or beliefs, and intelligence only provides bounds on their culpability or merit.
I’m somewhere near neutral on the question and I think most of it is due to confounding factors— education access, nutrition and health care, environmental factors, and culture. It’s almost impossible in my mind to get a pure genetic signal when there are so many factors that we also know affect intellectual capacity that I don’t think we have a smoking gun here. As a minor factor, maybe, but not with the importance some HBD types give it. You’d almost have to have a large cohort of swapped babies raised by other populations to really tease out the genetics. I don’t see anyone willing to do that.
As far as moral worth, I’m firmly in the equality camp. A human should have all the rights that go along with being human.
https://en.wikipedia.org/wiki/Minnesota_Transracial_Adoption_Study
Isn't this kind of it?
I mean yes, but I’d consider a 10-15 IQ test differential to be fairly minor, it’s one σ at best. It’s there, but unless you’re doing high level stuff, I don’t think most people would be able to tell the difference at a glance between IQ 115 and IQ 100. At lower IQ it makes a difference sure, but for average IQ levels it’s not that much.
AFAIK as IQ is deliberately intended to be normalized, the gap is exactly the same 85 to 100 as 100 to 115, and if you think that those two aren't the same, you are also inherently saying that IQ is the wrong tool for the job. That's not even getting into the whole "what benchmark do we set 100 at", do we update it year to year or try to peg it to some historical benchmark (though this is not necessarily fatal to IQ as a metric in the same way the first is, it does present a question that must be addressed when using IQ).
Simply using IQ necessitates that you grapple with these things. That's the nature of using numbers to describe something human. You, the invoker of IQ, need to prove the numbers work as numbers and aren't better being left as philosophical concepts or practical examples, or point to something well established that does. The simple, self-evident fact that IQ is fit to a normal curve and you yourself don't seem to believe that a symmetric 15 point gap is equal across the domain is in and of itself a tacit admission that IQ is the wrong tool. Are you familiar with the statistical notions of how an assigned number scale can be nominal, or ordinal, or interval, or ratio? It's not a perfect paradigm by any means, but it's one you must grapple with at least on some level, and happens to be incredibly relevant here in this case. See also OP's initial claim that the distribution has a weird asymmetric tail, also evidence (though more mild) against using IQ as the correct tool. Similarly, the fact that you dodge the 100-center question, which is a fundamentally important question to the use of IQ, is not acceptable.
I mean, I get the whole all models are wrong but some are useful, but these are just the very basics, the fundamentals, they are not nitpicks. An example of something that at least does attempt to address these issues and mostly succeeds is the Likert scale. You might be familiar with it. It's the classic 5 or 7 point scale in response to a question, with "strongly agree" and "slightly agree" and "not sure" and disagree options. There's a natural zero, and at least psychologists attempt to say that the distance between each point is "equal". I know forced normalization distorts this equal-distance formulation slightly, in terms of the math, but two properties that persist across the transformation are the aforementioned symmetry of responses, and also the center point of responses. These two decisions are non-negotiable and mandatory to make and cannot be hand-waved away. They are inherent to the math and the use of a numerical model.
Imagine two groups of children go to a themepark and want to hop onto a rollercoaster, which has a minimum height requirement of 105cm. One group of children has heights that range from 110cm to 150cm, and the other group has heights that range from 70cm to 110cm. The symmetric 40cm gap would not be equal across the domain - is this a tacit admission that height is the wrong measuring tool for the job? A simple 15cm boost would have a different effect on each group's ability to ride the rollercoaster, so how can you say that 1cm is equal to 1cm between the two groups?
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