The bottom line is, we are all biased. We all tend to think of women’s work as somewhat smaller, derivative, inferior. We do so unconsciously and involuntarily. We are not aware of it, nor do we notice it in others. That’s what all these studies are saying. It’s as if everyone is wearing glasses with the same tint. You’re wearing them even if you’re “open-minded” or “against discrimination”, even if you start your sentences with “I’m not against women, but…”
It is not, and never has been, only about a few individuals who forgot to catch up with the times. It’s not about trolls who say horrible things about women on unmoderated blogs. It’s about you, and me, and everyone we know. It’s about the nice, polite, progressive people who just wish that their female colleague down the hall didn’t try to be more ambitious than is good for her. (She’s clearly good, but does she really think she’s equal to X and Y? And she doesn’t have the same leadership quality, either.) It’s about that paper by two female authors that’s just not quite as groundbreaking as this other paper written by two men. In other words, you need to start by examining your own bias.
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Do you understand what it means for a group to be biased? I do, because I’ve seen it. It means that when you call out X on his behaviour, the rest of the group sides with X, who is their valuable, respected colleague and deserves every benefit of the doubt. Who knows, X might have even chaired some committee on equity. Surely he didn’t intend to be sexist, and anyway, he actually has a point about those MathSciNet numbers. You, on the other hand, are an uncollegial troublemaker who accuses nice people like X of horrible things. You’re overreacting, and you need to learn to work with people. And next time there is a similar conference, there is a chance that others more collegial and reasonable than you will be invited to organize it.
Izabella Laba is great. She is in the unenviable position of being one of a tiny number of women in the math blogosphere, and I’m impressed she can keep writing stuff like this without wanting to strangle everyone.
Regarding the more specific issue of gender bias in academia, I seem to recall reading about a study where it was found that senior mathematicians writing recommendation letters for male junior mathematicians tended to praise their mathematical work, but when writing recommendation letters for female junior mathematicians tended to praise their personalities.
The trouble with that study (and just about any other study of outcome equality) is that there’s no control for the possibility of actual inequality in the inputs. By the time someone is at the “receiving recommendations from senior mathematicians” stage they’ve been exposed to at least a decade or two of a potentially gender-biased environment. There’s nothing in the experiment here which distinguishes the hypothesis “senior mathematicians write biased recommendations” from “junior mathematicians received biased educations” or “young children receive biased levels of encouragement from family” or even plain “girls aren’t good at math”.
Just because poor studies are suggestive of bias doesn’t mean that good studies wouldn’t be too, though. The best evidence for gender bias in academia I’ve seen is that double-blind testing also shows gender bias in academia. If this Moss-Racusin et. al. experiment is replicable (and there isn’t anything obviously suspicious about it), the results are pretty damning.
Point. Whether or not it was well-designed, I thought it was a good example of how bias can (potentially) manifest in a way that doesn’t feel biased from the inside.
Possibly relevant:
“Gender Bias 101 for Mathematicians”.
A few quotes (but really, read the whole thing) —
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Izabella Laba is great. She is in the unenviable position of being one of a tiny number of women in the math blogosphere, and I’m impressed she can keep writing stuff like this without wanting to strangle everyone.
Regarding the more specific issue of gender bias in academia, I seem to recall reading about a study where it was found that senior mathematicians writing recommendation letters for male junior mathematicians tended to praise their mathematical work, but when writing recommendation letters for female junior mathematicians tended to praise their personalities.
The trouble with that study (and just about any other study of outcome equality) is that there’s no control for the possibility of actual inequality in the inputs. By the time someone is at the “receiving recommendations from senior mathematicians” stage they’ve been exposed to at least a decade or two of a potentially gender-biased environment. There’s nothing in the experiment here which distinguishes the hypothesis “senior mathematicians write biased recommendations” from “junior mathematicians received biased educations” or “young children receive biased levels of encouragement from family” or even plain “girls aren’t good at math”.
Just because poor studies are suggestive of bias doesn’t mean that good studies wouldn’t be too, though. The best evidence for gender bias in academia I’ve seen is that double-blind testing also shows gender bias in academia. If this Moss-Racusin et. al. experiment is replicable (and there isn’t anything obviously suspicious about it), the results are pretty damning.
Point. Whether or not it was well-designed, I thought it was a good example of how bias can (potentially) manifest in a way that doesn’t feel biased from the inside.