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Journal of Social Computing

Article Title

Pandemic Policymaking

Keywords

manifold learning, computational social science, congress, policymaking, COVID-19

Abstract

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.

Publisher

Tsinghua University Press

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