Counting Doesn’t Count Unless People Matter

Nearly every time I end up in a heated (and therefore likely, pointless) exchange about inequality and discrimination on The Internet, it disintegrates into an argument over numbers. Whose statistics are correct? How many Black folks were killed by police this past year? But how many Black folks were killed in homicides by other Black folks? Some insist on replying. How many COVID-19 deaths? But how many were elderly or disabled? Weren’t these people gonna die anyway? comes the response.

I’m sick and tired of the numbers. I’m sick and tired of the way we collectively insist on turning to the supposedly unbiased nature of science without simultaneously insisting on careful attention to systemic, unconscious bias in the methodology of our research and on careful contextualization of the results presented.

Counting doesn’t count unless it’s ultimately people who matter.

Historians and scientists trained in the theories now collected under the umbrella of postmodernism have argued at least since the 1990s that racism (and sexism, and heterocentrism, and ableism) is ingrained in much of the scientific methods on which we’ve relied as a society. For example, Emeritus Professor of the History of Science at Harvard University Katharine Park has published on gender biases in medicine, biology, and chemistry that have limited women and non-binary folks’ access to health and happiness. Angela Saini’s Superior: The Return of Race Science (Beacon Press, 2019) warns against the ways in which even…