USING COUNTERFACTUAL MODELING AND MACHINE LEARNING GENERATED PROPENSITY SCORES TO EXAMINE BLACK SOCIAL CONTROL AND MATHEMATICS

The Race, Gender, and Social Control in STEM (RGSC-STEM) Lab has established important and long overdue connections between state violence, schooling, and racial inequities in mathematics. RGSC-STEM work has been guided by the question of whether our national priority to fill the STEM pipeline in schools requires them to first drain the school to prison pipeline, since minoritized students are underrepresented in the former and overrepresented in the latter. Using data from the Educational Longitudinal Study 2002 and the High School Longitudinal Study 2009, this chapter highlights how the lab has sought to examine these concerns through methodologies that offer stronger inferences of association, if not causal inferences. The authors find Black students are at the greatest risk for surveillance and exclusionary discipline, and that both lower mathematics performance and the likelihood of college attendance. The discussion concludes by summarizing what RGSC-STEM Lab has learned related to policymaking, research, and justice.