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Vaniver comments on LW Women: LW Online - Less Wrong Discussion
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I think that gender is on topic when discussing fun theory, self-modification, and CEV, in ways that politics are on topic when discussing those things. I do agree that it might be worthwhile to try and rewrite articles that are problematic; the last I heard, the sequences were being edited to become a book, and that seems like a good time to attempt those changes.
Is good science more likely to match or smash stereotypes? If you believe that stereotypes are Bayesian evidence for the ground truth, then good science is more likely to match stereotypes, and thus, science that smashes stereotypes is less likely to be good science. Now, this is still just Bayesian evidence, and enough studies that are done well can outweigh the hastily-made impressions of the public. The neat thing about this is that we can quantify the amount that we should believe in stereotypes; the linked article suggests anti-believing in stereotypes, without explicit justification as to why.
When someone encourages science to smash stereotypes, they need to be clear what methodological principle they have in mind. Without that, it reads like a political rallying cry, supplemented with ammunition used to kill enemy soldiers, rather than a serious suggestion by an empiricist.
For example, consider this study, and its rapid promotion by feminists. It was a single study, which was sprinkled with warnings that a single study doesn't prove anything, and that this was, to the best of the authors' knowledge, the only time this result had ever been observed, despite widespread experimentation. Glancing at it briefly, I found several components of their results that looked odd, and warranted investigation.
Separating what one wants to be true and what one believes to be true is a very important rationality skill, which should be applied to gender just as much to the rest of life.
Depends on what you mean by "stereotype".
If everyone says that Welsh corgis weigh less than one ton, that is good evidence that they do weigh less than one ton.
However, if a group of loud Greens says that Blues are whiny, I am not so sure that this is good evidence that Blues are whiny. I think it is more likely to be something other than evidence — for instance, a rhetorical tactic to encourage Greens to steal Blues' stuff and discourage Blues from complaining about it.
I expect there to be plenty of low-quality motivated search. That is not surprising. I also expect that if Greens hold a stereotype about the lived experience of Blues that is contrary to Blues' reports of their own lived experience, the Greens' stereotype is screened off as evidence by the Blues' experience.
That ... really doesn't follow.
Suppose G is a binary variable of the ground truth, S is a binary variable of the stereotype, and E is a binary variable of the result of an experiment.
If stereotypes are Bayesian evidence for the ground truth, that means P(S|G)>P(S|~G) and P(~S|G)<P(~S|~G). By "good" science, I mean science that is Bayesian evidence for the ground truth, so P(E|G)>P(E|~G) and P(~E|G)<P(~E|~G). Thus, for most distributions we get P(E|S)>=P(E|~S), and P(~E|S)<=P(~E|~S). (If you don't see why this is, I recommend opening up a spreadsheet, generating some binary distributions which are good evidence, and then working out the probabilities through Bayes.)
It's not guaranteed to be the case, because stereotypes and the results of experiments are probably not independent once we condition on the ground truth. The important thing about using this as a criticism is noting that stereotypes prevalent in academia and stereotypes prevalent in the general population may be rather different. Looking at the suggested results in the linked article, you'll note it's saying "hey, you should conform to my stereotypes, even when the ground truth is probably the other way" under the guise of "smash stereotypes."