sexism as origin, not accident

[note: this post mentions rape and eugenics]

One of the most prominent proponents of the scientifically inaccurate idea that “male and female brains” are biologically, categorically distinct went on a podcast this week and retracted some of his rhetoric. That’s great news! But in his retraction, Simon Baron-Cohen says people incorrectly jump to the conclusion that his is a “very sexist theory” because they “haven’t bothered reading the book” or his articles. He’s wrong there—reading closely reveals plenty of evidence for sexism as the origin of his theory—but it raises a larger issue. Baron-Cohen is right that reading the original text is important, that the history of science and ideas matters. Without it, modern incarnations of eugenics, phrenology, scientific sexism, and more are able to present themselves as new and progressive ideas.

In this #exclusive #breaking interview, @sbaroncohen clarifies his position on his controversial extreme male brain theory “I think today is a useful opportunity to publicly retract some of that language about male & female brains”
#neurodiversity
⬇️⬇️⬇️https://t.co/3PAkyQJYIN pic.twitter.com/gDfNkRapj7

— John Offord (@johnofford808) November 24, 2020

research during covid (part 2): centering care in/with the mechanics of virtual fieldwork

The following is a guest post by Laura Mauldin. Part 1 is available here.

In the previous post I talked about care for ourselves as we embark on fieldwork during a pandemic, care for each other as fellow academics also trying to figure it out, and care for our participants too. To continue the conversation about how to best care for ourselves, each other, and our participants, this installment focuses on logistics. There have been a variety resources posted about what it means to strategize fieldwork and to be “in the field” during a pandemic. Deborah Lupton’s crowdsourced document has been a fantastic resource for students and faculty alike trying to re-define field work during COVID19, anthropologist Pam Block wrote about bearing witness for the Wenner-Gren Blog, and a post by Sharon Ravitch for Social Science Space emphasized trauma-informed methods and chronic illness methodology (both of which I engage in my own work).

research during covid (part 1): taking care of each other

The following is a guest post by Laura Mauldin.

During COVID19, qualitative researchers are having to improvise and use all kinds of new strategies for doing fieldwork. I’ll focus on some of mechanics of these strategies in part 2 of this series, but this installment is focused on care: It is imperative to care for each other as researchers right now. We need a collective act of care for our fellow qualitative researchers; we are all pressured and stressed and trying to scramble to do the best work we can. We are all learning to adjust to the new realities of fieldwork, but we need to be willing to talk about what adjustments we have made so that we can collectively add to the fund of knowledge about this adjustment. Let’s make it easier for each other by talking about the ways we’ve adjusted, and, when necessary, the ways these adjustments have failed or fallen short.

ai gender bias and computational social science

Gender bias is pervasive in our society generally, and in the tech industry and AI research community specifically. So it is no surprise that image labeling systems—tools that use AI to generate text describing pictures—produce both blatantly sexist and more subtly gender biased results. Our new paper, out now and open access in Socius, adds more examples to the growing literature on gender bias in AI. More importantly, it provides a framework for researchers seeking to either investigate AI bias or to use potentially biased AI systems in their own work.

Two images of U.S. Members of Congress with their corresponding labels as assigned by Google Cloud Vision. On the left, Steve Daines, Republican Senator for Montana. On the right, Lucille Roybal-Allard, Democratic Representative for California’s 40th congressional district. Percentages next to labels denote confidence scores of Google Cloud Vision.

partisans, not scientists, decide if science is partisan

Last month, Audra Wolfe wrote a fantastic post about how science is and always has been political. In the post, she analyzes statements from Nature and Scientific American, both of which endorsed Joe Biden in the 2020 Presidential Election, but which take different approaches. Nature argues that Biden will restore the centrality of science to governance and trust in science, while Scientific American focuses on how rejecting scientific guidance has hurt the public. Wolfe summarizes:

“Trust science” and “being guided by” scientific data are different things. One implies restoring scientists’ ability to work as autonomous professionals; the other implies that a Biden administration will take scientists’ advice into consideration along with other factors, including our obligations to one another and to the planet.

Wolfe’s analysis is great and I recommend you go read it and then come back. Good? Ok! I have two related thoughts that I want to try to clarify in this post: the relationship between something being “political” and something being “partisan” and the question of how something becomes partisan. Thinking through these distinctions has been useful for me as I try to parse my frustration around contemporary discourse about whether science should be political (in some sense) and what scientists should do in light of different answers to that question.