Impact of COVID-19 on the comparative practice of federalism: Some preliminary observations
Bibliographic record
Abstract
The COVID-19 pandemic is an unprecedented international event. The spread of the coronavirus – the biggest public health crisis in a century and the first of this scale in the globalized modern world – has prompted unparalleled responses by national governments. The proliferation of 24-hours news coverage and social media has allowed people across the world to follow, in real time, the unfolding and visible impacts of the pandemic. In 2020, as governments grappled with fluctuating waves of the COVID-19 pandemic, the effectiveness of public policy varied among federal nations (the paper focuses on countries that are explicitly and constitutionally federal, and countries with governance systems in which governance powers and responsibilities are devolved from the central level to the subnational level). Federal countries such as Australia and Canada managed to keep mortality low, whereas others such as Brazil, Spain and the United States suffered some of the highest numbers of fatalities anywhere around world, both in absolute and relative terms (Kontis et al. 2020, 1919-1928; Brunner et al. 2020; Ritchie et al. 2020)
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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 | 0.024 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 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".