COVID-19 Policy Response and the Rise of the Sub-National Governments
Bibliographic record
Abstract
We examine the roles of sub-national and national governments in Canada and the United States vis-à-vis the protective public health response in the onset phase of the global coronavirus disease 2019 (COVID-19) pandemic. This period was characterized in both countries by incomplete information as well as by uncertainty regarding which level of government should be responsible for which policies. The crisis represents an opportunity to study how national and sub-national governments respond to such policy challenges. In this article, we present a unique dataset that catalogues the policy responses of US states and Canadian provinces as well as those of the respective federal governments: the Protective Policy Index (PPI). We then compare the United States and Canada along several dimensions, including the absolute values of sub-national levels of the index relative to the total protections enjoyed by citizens, the relationship between early threat (as measured by the mortality rate near the start of the public health crisis) and the evolution of the PPI, and finally the institutional and legislative origins of the protective health policies. We find that the sub-national contribution to policy is more important for both the United States and Canada than are their national-level policies, and it is unrelated in scope to our early threat measure. We also show that the institutional origin of the policies as evidenced by the COVID-19 response differs greatly between the two countries and has implications for the evolution of federalism in each.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".