The Pandemic and Beyond: Federalism Faces Existential Threats
Bibliographic record
Abstract
The authors explore how Canadian federalism shapes government responses to the COVID-19 pandemic and other existential threats, such as climate change. The authors assess ways in which the division of power between the federal and provincial governments has been both a potential benefit and hinderance to successfully confronting the COVID-19 pandemic. They first consider ways in which decentralized provincial responses have been a strength, through tailored policy, innovation across provinces, and as a way to avoid centralized mistakes. They then consider how national responses nevertheless play a vital role, addressing aspects of risk that spill over across provinces, national economic risks, and allowing for equitable sharing of the burdens of existential threats like the COVID-19 pandemic. The authors also identify gaps in Canada’s federal structure which can undermine Canada’s response to existential threats: first, the potential for overlapping authority can lead to a lack of effective action; and second, the incomplete nature of Canadian federalism, can fail to integrate local and Indigenous governments as part of the response. The authors suggest that Canada’s response to existential threats ultimately relies on co-operative actions across all governments. While analysis of the response to the COVID-19 pandemic shows that this is possible within our federal structure, it does not always happen effectively. This will be an ongoing challenge as we move beyond the pandemic, but continue to face the threat of climate change.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".