The never-ending discussion: biology or bias?
Posted by Melissa on June 8, 2010
Opening up the NY Times web page today and reading John Tierney’s latest column, “Daring to Discuss Women in Science”, brought out an intense need to respond despite the pile of grading in front of me. Before diving into what I find so irritating about Tierney’s column, I will briefly note that the legislative proposal he mentions in the beginning of his article sounds potentially problematic, providing a source of ridicule or animosity without doing much to change the situation faced by women in science. Since I’m not well-informed on the details of the legislation, I’m not going to try to discuss it.
What I want to comment on is the rest of the article, which contributes to my general frustration with the ad nauseam back-and-forth of whether it’s biology or bias that accounts for the under-representation of women in the mathematically-intensive sciences. I tire of hearing about how women aren’t as adept at math, aren’t as good at spatial reasoning, and aren’t as willing to work hard. These discussions seem to rehash the same old arguments, and they don’t acknowledge the complexity of the problem, nor do they do much to change people’s inherent biases (or their beliefs that they are unbiased in the evaluation of the situation).
What I find most frustrating is that there are myriads of studies, and everyone can cite their favorite study to support their viewpoint — be it that bias is the dominant factor keeping women out of sciences or that biology accounts for the paucity of women. This spring, thanks to my colleague Joel Weisberg, I found what may currently be the best, though still imperfect, antidote to the never-ending, go-nowhere discussion of this topic, namely Stephen Ceci and Wendy William’s book, The Mathematics of Sex: How biology and society conspire to limit talented women and girls. The book is built on an extensive literature review of more than 400 studies on the role of biological and sociocultural factors in accounting for the low representation of women in math-intensive science fields. If he took the time to read The Mathematics of Sex, Tierney would find much of his argument is insufficient to account for the level of sex disparities found in fields like physics and engineering. Ceci and Williams review studies examining the male/female distribution at the extreme right tail of the distribution of math SAT scores, like the ones Tierney discusses, but they also acknowledge the importance of considering how the distribution varies with time period and with cultural geography, which shows flaws in using American student performance on the SAT as an indicator of why there aren’t more women in mathematically-intensive fields. While Tierney blithely contends, “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,” Ceci and Williams admit that current studies don’t sufficiently determine how one’s exact location on the extreme right end of the mathematical testing distribution corresponds to future success.
On the flip side, supporters of women in science may be disappointed to find that, according to Ceci and Williams’ survey, environmental factors such as stereotype threat or bias are also not sufficient to account for the poor representation of women in the sciences. Ceci and Williams provide a balanced consideration of just what research needs to be done to provide convincing evidence that either biological or sociocultural factors play more than a secondary role in accounting for the low numbers of women in mathematically intensive fields. If The Mathematics of Sex doesn’t find bias or biological ability as being primary factors in accounting for the numbers of women in mathematically-intensive fields, what do they find? Their answer is complex, and I’ll try to write more after I’ve finished my grading.