For a long time, most human subjects research was performed primarily on white men. This was especially the case for research intended to be generalizable towards everyone. To address this bias, many funding agencies now have policies that require gender equality, by which they mean that women and men should be included in all generalizable research (e.g., drug treatment trials, brain imaging, exercise physiology). However, this bias correction reinforces two problematic ideas: that sex is binary and that sex difference is attributable to biological difference. Policy change that doesn’t address the history and context that led to biased sample populations only exposes bias and doesn’t fix it. So how do we include the variables of sex and gender in a way that allows us to uncover the many ways it can complicate, enhance, and broaden our understanding? And how can we show that there are times when research on women and gender diverse people actually produces the most generalizable findings?
In this year’s METIS Lecture, Professor Kate Clancy and Professor Jenny Davis will offer case studies across several disciplines to show how gender and sex are entangled, and enrich scholarly study. They will then suggest several queer feminist interventions into problem definition, data collection, and data interpretation towards a broader recognition of how all data are relevant — not only those that fall within a “normal range” but outliers and even those from excluded categories.