CHAPTER A-5 Pandemic Data Sharing: How the Canadian Constitution Has Turned into a Suicide Pact
Bibliographic record
Abstract
For decades, public health professionals, scholars, and on multiple occasions, the Auditor General of Canada have raised warnings about Canada's dysfunctional system of public health data sharing.Current, timely, and complete epidemiological data are an absolutely necessary, but not sufficient, precursor to developing an effective response to the pandemic.Nonetheless, it remains true that nearly two decades after data sharing proved a catastrophic failure in the 2003 SARS epidemic, epidemiological data still are not shared between the provinces and the federal government.This is largely due to a baseless and erroneous belief that health falls purely within the jurisdiction of the provinces, despite the Supreme Court of Canada's clear conclusions to the contrary, which has misled Canada to rely on voluntary data sharing agreements with the provinces that are not merely ineffective, but actually inhibit data sharing.As outlined in this chapter, there is no reason for this to be the case, since Canada already possesses statutory powers, under the Statistics Act and the Public Health Agency of Canada Act, to oblige provinces to share critical epidemiological data in a timely manner.It must exercise those powers, both in response to
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.014 | 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".