manifold learning, computational social science, congress, policymaking, COVID-19
This study leverages a high dimensional manifold learning design to explore the latent structure of the pandemic policymaking space only based on bill-level characteristics of pandemic-focused bills from 1973 to 2020. Results indicate the COVID-19 era of policymaking maps extremely closely onto prior periods of related policymaking. This suggests that there is striking uniformity in Congressional policymaking related to these types of large-scale crises over time, despite currently operating in a unique era of hyperpolarization, division, and ineffective governance.
Tsinghua University Press
Philip D. Waggoner. Pandemic Policymaking†. Journal of Social Computing 2021, 2(1): 14-26.