Towards a New Intergovernmental Agreement on Early Pandemic Management
Bibliographic record
Abstract
The Canadian response to COVID-19 produced several problems that are at least partially attributable to a lack of coordination between the federal and provincial governments. The federal government has not taken on a strong coordinating role. Many provinces have ‘gone their own way’ even where uniform standards are necessary to minimize public health threats. While some believe the federal government should use its existing powers to coordinate a response, the federal government alone cannot address all possible concerns and there are strong political incentives for federal government not to unilaterally take a stronger role in pandemic management. This article accordingly motivates an intergovernmental agreement on pandemic preparedness and early pandemic responsiveness (viz., early pandemic management). An intergovernmental agreement is a more promising tool for securing the coordination necessary for good pandemic management than unilateral federal action or the status quo. A detailed agreement that clearly sets out who will do what when a pandemic is imminent/when a pandemic begins will clarify expectations in early pandemic management and incentivize compliance therewith, helping to secure much-needed coordination. Developing it in non-pandemic conditions should also ensure a more rational approach to pandemic management that improves health outcomes and better fulfills Canada’s moral and international legal obligations.
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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 | 0.128 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.017 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.033 | 0.031 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".