The Differential utilization of conditional sentences among Aboriginal offenders in Canada
Bibliographic record
Abstract
Canada's community-based custody sanction -the conditional sentence of imprisonment -came into effect in 1996 with major statutory reforms to the Criminal Code.While the new sanction was found to reduce incarceration rates among the general offender population, there has been no evidence that it resulted in decreases of incarceration among Aboriginal offenders.Now 20 years following its introduction, this study sets out to document recent trends in the use of conditional sentences and for the first time, focus on trends of Aboriginal offenders.Using a new metric, the Conditional Sentence Utilization (CSU) percent, the analyses reveal a shift in general utilization of the sanction.At the onset of the new millennium, Aboriginal offenders received a greater proportion of community-based imprisonment sentences.This pattern reversed in 2008/09 and for the next five years non-Aboriginals received a greater proportion of community based imprisonment sanctions.Analyses conducted at the provincial/territorial-level find widespread variation in the use of community custody among the two offending populations.In Quebec, Aboriginal offenders consistently receive conditional sentences in far greater proportion to non-Aboriginals.In Manitoba, the opposite was found.The implications of these findings on criminal justice policy are discussed.1 I would like to thank Professor Julian Roberts for comments on an earlier draft of this article. The (Differential) Utilization of Conditional Sentences among Aboriginal 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".