Bibliographic record
Abstract
Two years ago, when I became editor of Canadian Studies in Population, no one would have imagined that, only a year later, the world would be in the midst of the most devastating pandemic of modern times.Canada has so far experienced three waves (Fig. 1) and many provincial epidemics of the new coronavirus disease .At the end of its third wave (July 21, 2021), Canada ranked 7th in cases per capita, and 6th in COVID-19-related deaths per capita among high-income, medium-large peer countries (Australia, France, Germany, Italy, Japan, South Korea, Spain, Taiwan, the UK, the USA).These statistics, however, fail to demonstrate the heterogeneity in the provincial experiences of COVID-19.For example, the province of Québec had a per-capita COVID-19 mortality rate (130.9 per 100,000) close to France (165.5 per 100,000) (Little, 2021;Roser et al., 2021), while the provinces in the Atlantic "bubble" (Nova Scotia, New Brunswick, Prince Edward Island and Newfoundland and Labrador) and the Northern Territories have recorded fewer than 10,000 cases and 500 deaths (Little, 2021), thanks to the COVID elimination strategy they adopted since the beginning of the pandemic.These statistics reflect not only specific pandemic dynamics, but also different public health measures adopted at the provincial level.Although several improvements were made to improve federal provincial collaboration following the 2003 SARS outbreak (Webster, 2020), Canadian provinces have used their administrative authority over health care to forge individual responses to COVID-19.Despite early lockdowns in March 2020, the first wave of the pandemic hit Québec and Ontario particularly hard, where COVID-19 spread almost unchecked in long-term care facilities (
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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 | 0.011 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.015 | 0.022 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".