Am I missing part of the argument here? At the beginning of section IV, Scott argues that stereotypes can’t explain the gender gap in engineering because of the differential rate at which people major in math vs. engineering (45% women in math, 20% women in engineering). He says:

> Might girls be worried not by stereotypes about computers themselves, but by stereotypes that girls are bad at math and so can’t succeed in the math-heavy world of computer science? No. About 45% of college math majors are women, compared to (again) only 20% of computer science majors. Undergraduate mathematics itself more-or-less shows gender parity. This can’t be an explanation for the computer results.

Later, he introduces the thing-people interest spectrum and makes a case that it’s inherent. He then breaks down the gender gap in various medical fields in way that’s suggestive that the differences are due to the thing-people idea.

But, how does that apply math vs engineering or math vs. programming? Or for that matter, programming vs. chemical engineering or electrical engineering vs. chemical engineering? During undergrad, my recollection was that there were proportionally fewer women in electrical engineering than in chemical engineering in the classes I saw, and some quick googling seems to bear this out. If math vs. engineering is a mystery that can't be explained by streotypes, it also appears to be a mystery that can't be explained by thing-people. Although I think it's a long stretch, maybe you can argue that computers are more "thing-like" than math, but I don't think you can really push that argument through to explain the relative ratios in CS, math, EE, CivE, AE, MSE, and ChemE? BTW, the reason I think it's a stretch is because you could also argue that computers are more "people-like" than math, so you could flip the arugment around if the ratios were reversed. For EE vs. ChemE, maybe EE rates are depressed because there's a lot of cross-over between EE and BME classwork and BME is arguably more people-like, so the would-be EEs go into BME, but if there's crossover, maybe that makes EE more BME-like and therefore more people-like. I don't think you can give an explanation that's much stronger than a just-so story for some other set of observed ratios.

Sure, you can pick a subset of fields where thing-people appears to explain the variance[1], but you can also pick a set of fields where it doesn’t appear to explain the variance. Scott seems to view a set of counter-examples as a knockdown argument against stereotypes. But then why doesn’t this other set of examples invalidate the thing-people explanation he argues for? Why can’t you apply the exact same line of reasoning he applied to stereotypes to thing-people?

This line of reasoning seems internally inconsistent to me. Am I missing something that would make this line of reasoning consistent?

One line of reasoning is that Scott is merely rebutting someone else's argument and that he therefore doesn't need to explain what's going on and he only needs to explain why the other explanation is wrong. But in that case, there isn't a need to bring up thing-people. It seems like it's been brought up because Scott believes thing-people has more explanatory power than sterotypes and Scott is making a positive argument about thing-people, not just knocking down someone else's argument.

[1] Even within the fields he picks, one example he gives is the rate at which women go pediatrics at a higher rate than any other specialization he lists other than ob/gyn. But why is the rate in pediatrics so different than in psychiatry? He argues that they're both "people" fields, which sounds reasonable. But there's one field is 25% male and the other is 43% male. That's almost the same as the math/engineering difference he cites earlier with the genders flipped. Is dealing with babies somehow more people-like or less thing-like than talking to adults? It's certainly a strong stereotype that women are more interested in babies than men, but this is cited as an example of the explanatory power of thing-people, not of the explanatory power of stereotypes.