In my last post I wrote about the “unconscious bias” that many women in science both experience and harbor. This weekend at the ASM Microbe Conference in Boston, some of us witnessed that bias firsthand—in a rather poignant display, as it came straight from the American Society for Microbiology, an organization that is actively supporting women in science.

And it all came and went in a day. On the Sunday morning, I attended a fascinating session organized by ASM entitled “Forty Years After the Double Bind.” The Double Bind report was released in 1976 by the American Association for the Advancement of Science and detailed, as best it could, the landscape for minority women in science at that time. In many areas statistics simply weren’t available, but probably fewer than 2% of the science workforce was made up of minority women—while a number of qualified scientists, particularly Asian women, were unable to find employment.

Long story short: the session made clear that in these forty years we are nowhere near where we should be, for women, minorities, and least of all women minorities.

A highlight of the session was Alison Shaw’s “Leaks in the Pipeline.” IMG_2173This team from the University of Minnesota used data from the National Science Foundation and ran a simple predictive model to tell us how many tenured professors would be women today if hiring patterns were consistent between genders. (It should be roughly 30-35%, given that in 1980 only around 27% of graduates were women; see the blue streak in the top right figure.) Yet the reality is somewhere around 20% (blue triangles below that blue streak).

They then examined where the “leaks” in the pipeline from undergraduate studies to tenured professorship are occurring. Not surprisingly, it’s during the reproductive years that the women disappear (below right).

Another highlight: the economist Ernesto Reuben of Columbia University described his team’s experiment revealing both our ubiquitous unconscious bias and the benefits of “cheap talking” when trying to get hired for a job. In brief: in an experimental market in which subjects are hired to perform an arithmetic task that both genders perform equally well, when hirers receive no information other than a candidate’s appearance IMG_2174(making gender clear), both male and female hirers are twice more likely to hire a man than a woman. The discrimination persists if performance on the arithmetic task is self-reported, because men tend to boast about their performance (cheap talking), whereas women generally underreport it. Sadly, the discrimination is reduced, but not eliminated, when full information about previous performance on the task is provided. In short, patterns like these lead to “suboptimal hiring choices that remain biased in favor of men.”

So this was the session—a fantastic journey organized by a society that should be congratulated for its tireless efforts to close this stubborn gap. Yet it was paired with this bizarre news—and some very bad press for ASM—which reached me by email from a colleague just after I left the session.


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“ASM thinks your mother is dumb” was the subject heading. Indeed, apparently ASM had a made a short video to promote the conference in which attendees are challenged: “Can you explain your science in 30 seconds to your mom?” Well, it didn’t take long for the Twittersphere to light up in gentle disgust on that one. As many scientists rightly pointed out, “The question apparently assumes that moms are, well, not that smart. And that moms will look completely puzzled when you tell them your science. And that moms wear purple flowery dresses.” Many pointed out that their mothers were actually the scientists who inspired them to join the field.

You won’t be able to find the video anymore. ASM took down the link—and put up about ten tweets, and finally, an apology.

Sort of.

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1 Comment

  • Ursula Theuretzbacher says:

    Great post!
    The bias is so ubiquitous that it is hard to stay optimistic. The progress is very slow and in some scientific fields almost below detection limit.

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