On the Social Psychology of Sexism

As somebody interested in gender equality in CS, one thing that's proved quite illuminating for me is to read up on the psychology of sexism. Why does sexism persist in society? What social and psychological structures keep it in place?

Sexism in some ways is unlike other forms of discrimination. When it comes to race, or class, or disability, the social psychology literature will frequently talk about social distance. But when it comes to women, men "can't live without 'em" [1] -- and so things tend to be a bit different.

Ambivalent Sexism

It turns out that sexism has two faces: good old hostile sexism -- and the more palatable benevolent sexism. Benevolent sexism is the notion that women are wonderful, caring, nurturing and beautiful creatures -- and so must be protected and provided for. (Note the "creatures" -- not "people".)

The evidence on the psychology of sexism is that the people who espouse hostile sexism are also benevolently sexist. They think women shouldn't work -- women should stay home and care for the children because women are so good at mothering. And they get hostile when their regressive worldview clashes with attempts of women and men to change the status quo.

Furthermore, the more you're exposed to benevolent sexism, the more likely you are to later take on hostile sexist views. The more you're primed to believe that women should fill the magical-traditional role, the more likely you'll try to thwart any attempt to move away from this view.

A vital thing to note about benevolent sexism is how frequently it is embraced by women. Many women will argue that women are better at being nurturing, or communicating, etc. After all, why would you turn down a worldview that argues you're wonderful and worthy of protection?

Correspondence tests: uncovering biases against women in science

Part of the controversy surrounding affirmative action and other systems which give preferential treatment to minority groups comes from the ideal notion that people are judged on their merits -- and not their gender/race/etc [6]. In such an ideal world, for instance, a female scientist would be equally likely to be hired, given tenure, or accolades as an identical male scientist.

Science likes to bill itself as a meritocracy, in which scientists are evaluated only their work. A lot of the unease many scientists have about preferential treatment is that it goes against that ideal of meritocratic science [5]. So, it's worth asking: is a female scientist equally likely to be hired/tenured/etc as an identical male scientist?

Probably the best study design for probing this type of question are correspondence tests. These refer to studies where you describe either a female individual or a male individual to a group of participants -- keeping everything but gender (or race, ethnicity, etc) constant -- and see if participants respond differently to to the woman/man.

Correspondence tests are generally easier to run than audit studies, where you hire actors to be identical to one another except for gender/race/etc. Both types of studies are useful for identifying discrimination against particular groups. Another approach is to pair real male and female scientists with equal on-paper qualifications and see whether they are equally likely to be given tenure. This approach, however, suffers from the problem of pairing: are that female and male scientist really identical except for on-paper qualifications?

In this post, I'll be describing the results of three correspondence tests looking at discrimination against women in science. These three studies are also the only such studies that I know of to have been published since the 90s. (There's an older one from the 70s that is now a bit dated.)

Subtyping, Subgrouping, and Stereotype Change

There's been a fair bit of research finding that negative stereotypes are part of what deters women and racial minorities from computer science and STEM in general (e.g. [1]). These stereotypes make it harder for women and minorities to personally identify with computer science, and amplify some of the biases that they face in CS. So for this post, I'll be going over observed phenomena in social psychology and sociology that pertain to stereotype change.

Stereotypes

Stereotypes are really hard to change. They're reinforced from many sources (media, individuals, groups, etc). But even more than that, stereotypes are schema: they are how we mentally organize information about social groups, and how we can determine whether we are "in" or "out" of a group. Schema allow us to process information effortlessly, and are pretty deeply ingrained once they're there.

The human brain is not very good at changing schema. When we see evidence that contradicts our schema, our brains will do all sorts of mental gymnastics to avoid confronting or changing the incorrect schema. Most frequently, we forget that we saw it all. Sometimes our misconceptions even get stronger [2].

This happens with stereotypes. Betz and Sekaquaptewa did a study where they showed role models to young girls, to try to motivate the girls' interest in STEM [3]. Role models were either gender-neutral, or feminine. The result? Gender-neutral role models boosted interest -- and feminine (counterstereotypic) role models actually reduced girls' interest in science. To these girls, the feminine scientist -- a stereotype violator -- is aberrant.