The Data Will Not Save Us: Afropessimism and Racial Antimatter in the COVID Pandemic
Lead: Professor Anthony Hatch
This project is featured as part of the 2022 Robin M. Williams Distinguished Lecture from the Eastern Sociological Association and is now available as an Open Access article in a special issue of Big Data & Society on “Data, Power, and Racial Formation” guest edited by Kate Henne and Renee Shelby.
The Trump Administration’s governance of COVID-19 racial health disparities data has become a key front in the viral war against the pandemic and racial health injustice. In this paper, I analyze how the COVID-19 pandemic joins an already ongoing racial spectacle and system of structural gaslighting organized around “racial health disparities” in the United States and globally. The field of racial health disparities has yet to question the domain assumptions that uphold its field of investigation; as a result, the entire reform program called for by racial health disparities science is already featured on the menu of the white supremacist power structure. The societal infrastructure that produces scientific knowledge about patterns of health and disease in the human population needs to confront its structural position as part of the racial spectacle organized around racial health disparities in the United States. This paper offers an interpretation of racial antimatter to explain why the data will not save us in the COVID-19 pandemic, drawing on articulations of racial spectacle and structural gaslighting within critical race theory and Afropessimist thought. By positioning events in the COVID 19 pandemic together within the same racially speculative frame, I show how the collection of racial health disparities data came up against white supremacists’ political ambitions in a time-space where the demand for human life to matter and the iterative regeneration of racial antimatter collided. This paper highlights the need for ongoing analysis of the unfolding and future spectacles organized around racial health disparities.