Judging in the Time of a Pandemic:The Impact of COVID-19 on Bail and Sentencing in Canada
Bibliographic record
Abstract
At “the end of 2019 the World Health Organization was alerted to several cases of pneumonia in Wuhan, China, caused by an unknown virus. On 7 January 2020, China advised the world that a new coronavirus was the cause, later labelled SARS-CoV-2. It causes the disease known as COVID-19. In mid-January 2020, the Public Health Agency of Canada activated the Emergency Operation Centre in support of Canada’s response to COVID-19. On 22 January 2020, Canada implemented COVID-19 screening requirements for travelers returning from China. On 25 January 2020, Canada confirmed its first case of COVID-19 related to travel from Wuhan, China. On 9 March 2020, Canada recorded its first death related to COVID-19” (see Taylor v. Newfoundland and Labrador, 2020 NLSC 125, at paragraphs 29 to 31).
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.037 | 0.007 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".