Pandemic Policymaking  

作　　者：Philip D.Waggoner 

机构地区：[1]niversity of Chicago,Chicago,IL 60637,USA,and also with Columbia University,New York,NY 10027,USA.

出　　处：《Journal of Social Computing》2021年第1期14-26,共13页社会计算（英文）

摘　　要：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.

关 键 词：manifold learning computational social science CONGRESS POLICYMAKING COVID-19 

分 类 号：G51[文化科学—教育学]