Les inégalités ethnoraciales face au système de justice pénale et la démocratie : une analyse du cas canadien
Bibliographic record
Abstract
Cet article se propose d’étudier les enjeux soulevés par les inégalités ethnoraciales face au système de justice pénale dans un contexte démocratique, à partir d’une analyse en deux étapes. Nous esquissons d’abord un portrait de ces inégalités au Canada. Nous mettons ensuite en lumière les conséquences de ces inégalités et, notamment, les manières dont elles contreviennent aux principes structurants de la démocratie libérale. Nous concluons en examinant des pistes de solution identifiées dans la littérature sur le sujet pour contrer ces inégalités et, par extension, pour remédier à la perte de confiance qu’elles peuvent provoquer à l’égard du système de justice pénale et, plus largement, de la société dans laquelle les personnes et les communautés concernées vivent.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".