Sentencing Reform in Canada: Recent Developments
Bibliographic record
Abstract
Changes to the sentencing process in Canada are finally imminent. A number of reports in recent years have called for reforms in the area of sentencing and parole. In 1987, the Canadian Sentencing Commission released its final report Sentencing Reform: A Canadian Approach. This was followed in 1988 by the report of the Daubney Committee following its investigation into sentencing and parole. In addition to these proposals, the now-defunct Law Reform Commission of Canada, the Department of Justice and the Ministry of the Solicitor General all published reports containing reform proposals. In this article, the authors review recent events in the area of sentencing since the publication of the report of the Canadian Sentencing Commission. After a brief introduction, four principal policy issues are examined: (i) statutory statements of sentencing purpose; (ii) sentencing guidelines; (iii) the future of release on parole; (iv) the creation of a permanent sentencing commission for Canada. For each issue, the article critically examines the position taken by major players in the area of criminal law reform. The article concludes with a brief examination of Bill C-90, which recently received first reading, and which will be the object of further parliamentary scrutiny in the fall of 1992. In a subsequent article, the authors offer their own proposals to reform the sentencing of offenders in Canada.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".