Ok first, when naming things I think you should do everything you can to not use double-negatives. So you should say "gym average" or "no gym average". Its shorter, and much less confusing.

Second, I'm still confused. Translating what you said, we'd have "no gym removed average" -> "gym average" (since you remove everyone who doesn't go to the gym meaning the only people remaining go to the gym), and "gym removed average" -> "no gym average" (since we're removing everyone who goes to the gym meaning the only remaining people don't go to the gym).

Therefore we have,

gym average = no gym removed average < gym removed average = no gym average

So it looks like the gym doesn't help, since those who don't go to the gym have a higher average number of pushups they can do than those who go to the gym.

Note: You can verify this is the case by filtering for male respondents with male partners and female respondents with female partners in the survey data

I think the math works out to be that the variation is much more extreme when you get to much more extreme probabilities. Going from 4% to 8% is 2x profits, but going from 50% to 58% is only 1.16x profits.

This seems likely to depend on your preferred style of research, so what is your preferred style of research?

And then if we say the bottleneck to meritocracy is mostly c rather than a or b, then in fact it seems like our society is absolutely obsessed with making our institutions highly accessible to as broad a pool of talent as possible. There are people who make a whole career out of just advocating for equality.

I work at GDM so obviously take that into account here, but in my internal conversations about external benchmarks we take cheating very seriously -- we don't want eval data to leak into training data, and have multiple lines of defense to keep that from happening.

What do you mean by "we"? Do you work on the pretraining team, talk directly with the pretraining team, are just aware of the methods the pretraining team uses, or some other thing?

More to the point, I haven't seen people try to scale those things either. The closest might be something like TripleByte? Or headhunting companies? Certainly when I think of a typical (or 95th-99th percentile) "person who says they care a lot about meritocracy" I'm not imagining a recruiter, or someone in charge of such a firm. Are you?

I think much of venture capital is trying to scale this thing, and as you said they don't use the framework you use. The philosophy there is much more oriented towards making sure nobody falls beneath the cracks. Provide the opportunity, then let the market allocate the credit.

That is, the way to scale meritocracy turns out to be maximizing c rather than the other considerations you listed, on current margins.

Also this conclusion is highly dependent on you, who has thought about this topic for all of 10 minutes, out-thinking the hypothetical people who are actually serious about meritocracy. For example perhaps they do more one-on-one talent scouting or funding, which is indeed very very common and seems to be much more in-demand than psychometric evaluations.

Given that ~ no one really does this, I conclude that very few people are serious about moving towards a meritocracy.

The field you should look at I think is Industrial and Organizational Psychology, as well as the classic Item Response Theory.

I suspect the vast majority of that sort of name-calling is much more politically motivated than based on not seeing the right slogans. For example if you go to Pause AI's website the first thing you see is a big, bold

and AI pause advocates are constantly arguing "no, we don't actually believe that" to the people who call them "luddites", but I have never actually seen anyone change their mind based on such an argument.