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Journal of Social Computing
Article . 2021 . Peer-reviewed
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Journal of Social Computing
Article . 2021
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Pandemic Policymaking

Authors: Philip D. Waggoner;

Pandemic Policymaking

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.

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Keywords

Social sciences (General), H1-99, covid-19, manifold learning, Electronic computers. Computer science, policymaking, computational social science, QA75.5-76.95, congress

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
8
Top 10%
Average
Top 10%
gold