And in any case, the results are actually more complicated than the headlines suggest and still provide damning evidence that data gaps in school curriculums are teaching children biases. When children start school they draw roughly equal percentages of male and female scientists, averaged out across boys and girls. By the time children are seven or eight, male scientists significantly outnumber female scientists. By the age of fourteen, children are drawing four times as many male scientists as female scientists. So although more female scientists are being drawn, much of the increase has been in younger children before the education system teaches them data-gap-informed gender biases.
There was also a significant gender difference in the change. Between 1985-2016, the average percentage of female scientists drawn by girls rose from 33% to 58%. The respective figures for boys were 2.4% and 13%. This discrepancy may shed some light on the finding of a 2016 study which found that while female students ranked their peers according to actual ability, male biology students consistently ranked their fellow male students as more intelligent than better-performing female students.46 Brilliance bias is one hell of a drug. And it doesn’t only lead to students mis-evaluating their teachers or each other: there is also evidence that teachers are mis-evaluating their students.
Several studies conducted over the past decade or so show that letters of recommendation are another seemingly gender-neutral part of a hiring process that is in fact anything but.47 One US study found that female candidates are described with more communal (warm; kind; nurturing) and less active (ambitious; self-confident) language than men. And having communal characteristics included in your letter of recommendation makes it less likely that you will get the job,48 particularly if you’re a woman: while ‘team-player’ is taken as a leadership quality in men, for women the term ‘can make a woman seem like a follower’.49 Letters of recommendation for women have also been found to emphasise teaching (lower status) over research (higher status);50 to include more terms that raise doubt (hedges; faint praise);51 and to be less likely to include standout adjectives like ‘remarkable’ and ‘outstanding’. Women were more often described with ‘grindstone’ terms like ‘hard-working’.
There is a data gap at the heart of universities using teaching evaluations and letters of recommendation as if they are gender neutral in effect as well as in application, although like the meritocracy data gap more broadly, it is not a gap that arises from a lack of data so much as a refusal to engage with it. Despite all the evidence, letters of recommendation and teaching evaluations continue to be heavily weighted and used widely in hiring, promoting and firing, as if they are objective tests of worth.52 In the UK, student evaluations are set to become even more important, when the Teaching Excellence Framework (TEF) is introduced in 2020. The TEF will be used to determine how much funding a university can receive, and the National Students Survey will be considered ‘a key metric of teaching success’. Women can expect to be penalised heavily in this Excellent Teaching new world.
The lack of meritocracy in academia is a problem that should concern all of us if we care about the quality of the research that comes out of the academy, because studies show that female academics are more likely than men to challenge male-default analysis in their work.53 This means that the more women who are publishing, the faster the gender data gap in research will close. And we should care about the quality of academic research. This is not an esoteric question, relevant only to those who inhabit the ivory towers. The research produced by the academy has a significant impact on government policy, on medical practice, on occupational health legislation. The research produced by the academy has a direct impact on all of our lives. It matters that women are not forgotten here.
Given the evidence that children learn brilliance bias at school, it should be fairly easy to stop teaching them this. And in fact a recent study found that female students perform better in science when the images in their textbooks include female scientists.54 So to stop teaching girls that brilliance doesn’t belong to them, we just need to stop misrepresenting women. Easy.
It’s much harder to correct for brilliance bias once it’s already been learnt, however, and once children who’ve been taught it grow up and enter the world of work, they often start perpetuating it themselves. This is bad enough when it comes to human-on-human recruitment, but with the rise of algorithm-driven recruiting the problem is set to get worse, because there is every reason to suspect that this bias is being unwittingly hardwired into the very code to which we’re outsourcing our decision-making.
In 1984 American tech journalist Steven Levy published his bestselling book Hackers: Heroes of the Computer Revolution. Levy’s heroes were all brilliant. They were all single-minded. They were all men. They also didn’t get laid much. ‘You would hack, and you would live by the Hacker Ethic, and you knew that horribly inefficient and wasteful things like women burned too many cycles, occupied too much memory space,’ Levy explained. ‘Women, even today, are considered grossly unpredictable,’ one of his heroes told him. ‘How can a [default male] hacker tolerate such an imperfect being?’
Two paragraphs after having reported such blatant misogyny, Levy nevertheless found himself at a loss to explain why this culture was more or less ‘exclusively male’. ‘The sad fact was that there never was a star-quality female hacker’, he wrote. ‘No one knows why.’ I don’t know, Steve, we can probably take a wild guess.
By failing to make the obvious connection between an openly misogynistic culture and the mysterious lack of women, Levy contributed to the myth of innately talented hackers being implicitly male. And, today,
