Summers-Time Class: Gender Studies
Discussing potential differences in innate abilities between groups remains a controversial topic and research area. Famously, Larry Summers found himself at the center of a nationwide controversy after he upset many of the students and faculty at Harvard when he speculated on the reasons why women might be less represented in math and science fields. John Tierney in The New York Times has written two columns (here and here) that present some research along with his opinions which seem to vindicate Summers.
The Duke researchers report in Intelligence, “Our data clearly show that there are sex differences in cognitive abilities in the extreme right tail, with some favoring males and some favoring females.”
The researchers say it’s impossible to predict how long these math and science gender gaps will last. But given the gaps’ stability for two decades, the researchers conclude, “Thus, sex differences in abilities in the extreme right tail should not be dismissed as no longer part of the explanation for the dearth of women in math-intensive fields of science.”
Other studies have shown that these differences in extreme test scores correlate with later achievements in science and academia. Even when you consider only members of an elite group like the top percentile of the seventh graders on the SAT math test, someone at the 99.9 level is more likely than someone at the 99.1 level to get a doctorate in science or to win tenure at a top university.
The gap in science seems due mainly to another difference between the sexes: men are more interested in working with things, while women are more interested in working with people. There’s ample evidence — most recently in an analysis of surveys of more than 500,000 people — that boys and men, on average, are more interested in inanimate objects and “inorganic” subjects like math and physics and engineering, while girls and women are more drawn to life sciences, social sciences and other “organic” careers that involve people and seem to have direct social usefulness.
You can argue how much of this difference is due to biology and how much to society, but could you really affect it by sending scientists and engineers off to the workshops mandated by the bill now in Congress?
PZ Myers isn’t too happy with Tierney.
Now here’s the problem: there is no clear marker or metric for success in science. It’s a complicated task, with lots of variables and lots of different strategies for doing well. It’s not like looking for the person who runs the 100 meter dash the fastest, in which we could just line up the applicants, fire a starting gun, and give the job to the first person who crosses the finishing line. So what do we do? We use proxy metrics.
The best proxies are measurements that most closely approximate performance in science. We look at publication records, grants awarded, recommendations of colleagues, the sort of thing we’d expect our new scientist to continue doing. It’s not perfect — maybe the applicant is a neurotic living on the edge who’s about to break down, or maybe they have an abrasive personality that will affect the performance of other faculty — but it’s a good start. It’s what most committees should evaluate most highly in the hiring process.
All of those things are still just proxies for the constellation of properties you want in a scientific colleague. We have to balance them to get an idea of the potential of an applicant: it would be insane to hire someone with no experience, no publications, and no grants just because they got straight As in high school and college. But for some reason, in this tedious argument about the suitability of women to do science, all that gets mentioned is a gender difference in performance on standardized tests.
He then goes on to point out that wealth is also a great predictor of success in test scores. He’s using this example to blow up Tierney’s connection between gender and test scores since obviously wealth isn’t innate. Myers makes some interesting points but this is a poor one. It’s entirely possible (and researchers have made the point before (e.g. Steven Pinker)) that intelligence and wealth are correlated because, unsurprisingly, intelligent people are more likely to be wealthy due to their intelligence helping them get higher paying jobs. In other words, intelligence is a cause of wealth rather than the reverse.
Real discrimination is a problem, but pretending that no differences exist makes it more difficult to establish when actual discrimination is taking place. At the very least we should be able to discuss the issue without being vilified. If not we might have to sue Mother Nature for age discrimination.