April 22, 2014 | 15
[Editor's note: Following criticism of this post on social media, Scientific American posted the this statement.]
This is a guest post from my friend Chris Martin. Chris (chriscmartin.com, @ChrisMartin76 on Twitter) studied psychology and music at Davidson College, human-computer interaction at Georgia Tech, and psychology at the College of William and Mary. He is currently in a sociology doctoral program at Emory University, where he primarily conducts research on psychological well-being, but also studies attitudes toward affirmative action, the misperception of a White social ceiling, and other social psychological topics.
Chris thought of writing this post partly in response to a viral video documenting a characteristically articulate reply by Neil DeGrasse Tyson on the so-called “Larry Summers question”. The question was rather ill formed but Tyson made an excellent point about how – based on his own experience – women and minorities in the sciences are often expected by default to operate within the parameters of the paradigm defined by those in authority. However he still did not address the original question about Larry Summers and that’s what Chris wanted to address, especially since he thought that people who only watched the Tyson video may come away with incomplete and misleading information.
Recently, a panel of scientists was asked the “Larry Summers question” – why are there fewer women in science? For background, this is called the Larry Summers question because of this portion of the speech that Summers delivered at Harvard:
“It does appear that on many, many different human attributes-height, weight, propensity for criminality, overall IQ, mathematical ability, scientific ability-there is relatively clear evidence that whatever the difference in means-which can be debated-there is a difference in the standard deviation, and variability of a male and a female population. And that is true with respect to attributes that are and are not plausibly, culturally determined. If one supposes, as I think is reasonable, that if one is talking about physicists at a top twenty-five research university, one is not talking about people who are two standard deviations above the mean. And perhaps it’s not even talking about somebody who is three standard deviations above the mean. But it’s talking about people who are three and a half, four standard deviations above the mean in the one in 5,000, one in 10,000 class.”
Summers was right about this. Unfortunately, he has been misrepresented over and over again as having presented just one (rather than three) explanations for the gender gap; as having claimed that discrimination no longer exists; and for having said that there’s a difference in average ability. (If only we had accountability in journalism!)
Neil deGrasse Tyson responded to the question quite well, but since he’s not a social scientist, he wasn’t able to draw on psychological research on gender differences. His answer focused on stereotyping and self-fulfilling prophecy effect. I don’t blame him the slightest for lacking expertise in an area outside his specialty, but I do think people who only watch that video could come away with a misconception about the impact of stereotyping. I’m not going to discuss self-fulfilling prophecies here—they have a weak effect—but I will talk about how recent research has addressed this question.
To begin, it’s important to focus on how the question is typically posed in the 2010s: Why is there a shortage of women in the STEM disciplines? Like BRIC–and more recently, MINT—STEM is a cute acronym, but it doesn’t denote a connected set of fields. For one thing, the T stands for technology and the E stands for engineering, but one typically gets an engineering degree at an institute of technology—Cal Tech, MIT, Virginia Tech, and Georgia Tech. (Who overlooked this?) More significantly, math (M) connects to every empirical discipline, and even some non-empirical ones, so it doesn’t need to be lumped with the sciences. So I think we need to retire “STEM.”
Instead, let us limit ourselves to the sciences, which encompass both natural sciences and social sciences. Here are statistics on the sex ratio among graduate students. The order here is by level of analysis (with computer science thrown in somewhat arbitrarily next to physics). The first number is men; the second number is women.
Physics: 1694: 448
Computer Science: 1465: 380
Chemistry: 1520: 897
Anthropology: 186: 360:
Sociology: 230: 400
Political Science: 422: 303
For simplicity I’m excluding applied sciences—like engineering—but off the top of my head I can think of a few applied sciences where women outnumber men: medicine, veterinary medicine, public health. What these stats indicate is that there isn’t an aggregate gender divide that is replicated across all the sciences. –there isn’t some massive prejudicial force that prevents women from entering science in general.
This brings us to two related questions: Why is the percentage of women somewhat proportional to the “socialness” of the science? And why don’t women choose academic careers after they finish graduate school? To answer these questions, it’s worth looking at Steven Pinker’s contribution to the post-Larry Summers debate at Harvard. The full debate is also worth reading in full—and I apologize for giving Elizabeth Spelke, Pinker’s opponent, short shrift here–but this is Pinker’s summary of the psychological differences between men and women:
1. Men, on average, prioritize status, while women weigh status and family equally.
2. Women, on average, are more interested in people; men are more interested in things and abstract rule systems.
3. Men are by far the more reckless sex.
4. Men, on average, have a superior ability to do three-dimensional mental transformations.
5. Men, on average, are superior at mathematical reasoning.
6. Men have more variability than women across traits, which means that men are over-represented in the upper and lower tails of ability distributions.
Even though this debate occurred about a decade ago, Pinker’s points hold up quite well. For an explanation of these differences –especially 1,2,3, and 6–that’s rooted in natural selection, I’d recommend the latest edition of Anne Campbell’s “A Mind of Her Own”. Anne Campbell is both a feminist and an evolutionary psychologist—there are numerous feminists who study the evolution of sex differences—and I mention Campbell’s book because the evolutionary explanation is beyond the scope of this blog post.
If we look at the first difference in particular—that men chase status at the expense of their relationships—we find one reason that men are not turned off by the brutal publish-and-perish culture of higher academia. The greater difference in risk taking may not carry as much impact, except in empirical research of a high-risk high-reward nature.
However, two of Pinker’s points need some revision. The dichotomy between “people” and “things” needs just a minor tweak. Women, on average, don’t seem to be more interested in people per se, but rather they do seem more interested in the natural world. On average, they also have a stronger nurturing tendency than men, because through evolutionary history a non-trivial number of men abandoned their children, leaving women to raise their children. Although I’m just speculating here, this might explain why women show more interest in veterinary medicine than human medicine, animals being childlike in their behavior.
More important, the point about mathematical reasoning seems wrong given new evidence. It’s the presence or absence of other strengths that seems to matter. Quite simply, women who have mathematical aptitude tend to also have non-mathematical aptitude. These women are probably drawn to fields like social and personality psychology, where both types of aptitude matter. However, men who have mathematical aptitude (and interest) tend to solely have this aptitude. To quote psychologists Jeffrey Valla and Stephen Ceci: “Asymmetry in interests and aptitudes is an underappreciated factor in sex differences in career choice. To the extent this is true, focusing on strengthening young women’s STEM-related abilities and ability self-concepts to increase female STEM representation may be an unproductive approach; to increase representation, it may be more effective to focus on harvesting the potential of those girls and women whose breadth of interest and high ability spans social/verbal and spatial/numerical domains.”
This approach has a limitation–we can’t choose the scientific phenomena that the universe contains. Putting multiverses aside, we may find many of these phenomena simply require numerical skills to be understood. Nevertheless, it’s a worthy approach for people who find non-identical sex ratios to be problematic, regardless of whether discrimination exists. (I don’t share that point of view, but I’m willing to submit to the democratic process in academia.)
The point of focusing on innate psychological differences is not to draw attention away from anti-female discrimination. The research clearly shows that such discrimination exists—among other things, women seem to be paid less for equal work. Nor does it imply that the sexes have nothing in common. Quite frankly, the opposite is true. Nor does it imply that women—or men—are blameworthy for their attributes.
Rather, the point is that anti-female discrimination isn’t the only cause of the gender gap. As we learn more about sex differences, we’ve built better theories to explain the non-identical distribution of the sexes among the sciences. Science is always tentative, but the latest research suggests that discrimination has a weaker impact than people might think, and that innate sex differences explain quite a lot. Neil deGrasse Tyson’s explanation, which admittedly wasn’t about gender in the first place, relies solely on the socialization model, a model that no longer holds water. To quote Campbell, “Evolution didn’t stop at the neck.” It affected our brains, differentiating the average man from the average woman.
Related References and Videos
Gender Differences and Similarities – Janet Hyde (Annual Review of Psychology)
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