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by throwaway19937
1953 days ago
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I'm generally a fan of SSC but the second link is missing some significant factors on why law and medicine have become open to women. > "This makes no sense. There were negative stereotypes about everything! Somebody has to explain why the equal and greater negative stereotypes against women in law, medicine, etc were completely powerless, yet for some reason the negative stereotypes in engineering were the ones that took hold and prevented women from succeeding there." There were class action lawsuits that required law firms, law schools, medical schools, and hospitals to accept women doctors and lawyers. After sexism was recognized as a problem law schools used affirmative action to admit gender-balanced classes and law firms hired equal numbers of men and women. STEM subjects didn't have these interventions so it's unsurprising that sexism is more of an issue than in other fields. |
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My point was at the meta level that we should be using facts and evidence when we talk about this rather than saying “we just know we are right.” as the original commenter did.
Worth noting though that your theory doesn’t actually have enough explanatory power to explain the interesting part of the data; one of the points in Grant’s original article (which Scott is arguing against) was this juicy graph: https://media-exp1.licdn.com/dms/image/C4E12AQGEJuKqIh95Ng/a...
Note that female participation in CS increases along with other fields in the 70s, then something happens in 84/85 and participation plummets. Your theory would support a graph where CS never tracked with those other fields. But this is as clear an exogenous event as you are going to see in social science data.
What happened in 84? Maybe there is an explanation in the affirmative action caseload? I didn’t look at that dimension but your theory (fleshed out with data) might shed some light on that. (Also note that this graph looks worse than it really is; total CS enrollment also plummeted in 84 due to a recession and so there is a confounding effect there.)
Again, this is why data is so important in these discussions. The reality is way more complex than the “we know we are right” crowd appreciate; if you get this wrong then you won’t be able to fix the problem (or even identify the real problem).