Federalism in a Time of Plague: How Federal Systems Cope With Pandemic
Bibliographic record
Abstract
This article compares and contrasts the responses of Australia, Canada, Germany, and the United States to the COVID-19 outbreak and spread. The pandemic has posed special challenges to these federal systems. Although federal systems typically have many advantages—they can adapt policies to local conditions, for example, and experiment with different solutions to problems—pandemics and people cross regional borders, and controlling contagion requires a great deal of national coordination and intergovernmental cooperation. The four federal systems vary in their relative distribution of powers between regional and national governments, in the way that health care is administered, and in the variation in policies across regions. We focus on the early responses to COVID-19, from January through early May 2020. Three of these countries—Australia, Canada, and Germany—have done well in the crisis. They have acted quickly, done extensive testing and contact tracing, and had a relatively uniform set of policies across the country. The United States, in contrast, has had a disastrous response, wasting months at the start of the virus outbreak, with limited testing, poor intergovernmental cooperation, and widely divergent policies across the states and even within some states. The article seeks to explain both the relative uniform responses of these three very different federal systems, and the sharply divergent response of the United States.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| 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".