Pandemic Policymaking†
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 prio...
| Main Author: | |
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2021-03-01
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| Series: | Journal of Social Computing |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.23919/JSC.2021.0005 |
| _version_ | 1797986185112453120 |
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| author | Philip D. Waggoner |
| author_facet | Philip D. Waggoner |
| author_sort | Philip D. Waggoner |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T07:30:02Z |
| format | Article |
| id | doaj.art-b0160508203646589f19a2667bf28f36 |
| institution | Directory Open Access Journal |
| issn | 2688-5255 |
| language | English |
| last_indexed | 2024-04-11T07:30:02Z |
| publishDate | 2021-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Journal of Social Computing |
| spelling | doaj.art-b0160508203646589f19a2667bf28f362022-12-22T04:36:56ZengTsinghua University PressJournal of Social Computing2688-52552021-03-0121142610.23919/JSC.2021.0005Pandemic Policymaking†Philip D. Waggoner0<institution>University of Chicago</institution>, <city>Chicago</city>, <state>IL</state> <postal-code>60637</postal-code>, <country>USA</country>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.https://www.sciopen.com/article/10.23919/JSC.2021.0005manifold learningcomputational social sciencecongresspolicymakingcovid-19 |
| spellingShingle | Philip D. Waggoner Pandemic Policymaking† Journal of Social Computing manifold learning computational social science congress policymaking covid-19 |
| title | Pandemic Policymaking† |
| title_full | Pandemic Policymaking† |
| title_fullStr | Pandemic Policymaking† |
| title_full_unstemmed | Pandemic Policymaking† |
| title_short | Pandemic Policymaking† |
| title_sort | pandemic policymaking† |
| topic | manifold learning computational social science congress policymaking covid-19 |
| url | https://www.sciopen.com/article/10.23919/JSC.2021.0005 |
| work_keys_str_mv | AT philipdwaggoner pandemicpolicymaking |