Sursis, récidive et réinsertion sociale : un équilibre précaire
Bibliographic record
Abstract
The conditional sentence has been called a “hybrid” sentence because it aims at punitive and rehabilitative objectives simultaneously. In this study, we contrast the effects of punitive and rehabilitative conditions on breaches, revocations, recidivism, and social re-entry. The study is based on secondary data analysis. Files of male offenders who were given a one-year conditional sentence in the province of Quebec between 1 April 2003 and 31 March 2004 were analysed (N = 290). Findings indicate that punitive conditions have no impact on rates of recidivism and social re-entry but increase the likelihood that breaches will be detected. Repeated violations, in turn, increase the odds of revocation. At the same time, rehabilitative conditions were found to lower the risk of breaches, facilitate social re-entry, and, as a result, reduce recidivism. Rehabilitation and social re-entry, then, should continue to be the main goal of conditional sentences.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".