Progressive Reforms or Maintaining the Status Quo? An Empirical Evaluation of the Judicial Consideration of Aboriginal Status in Sentencing Decisions
Bibliographic record
Abstract
Government statistics and empirical research have long documented the fact that Aboriginal people account for a much higher proportion of Canada's inmate population than would be expected by looking at their relative proportion in the general population. In spite of this increasing focus on overrepresentation, there has been little documented change in Aboriginal incarceration rates. Section 718.2(e) was added to the Criminal Code to encourage judicial consideration of alternatives to incarceration, with particular attention to the circumstances of Aboriginal offenders. To evaluate the impact of section 718.2(e), the current study examined 691 randomly sampled sentencing decisions from the Quicklaw database to determine the extent to which Aboriginal status was correlated with judges’ sentencing decisions relative to other legally relevant factors that have traditionally guided sentencing. Hierarchical logistic regression analyses indicated that Aboriginal status did not significantly predict the likelihood of receiving a custodial or non-custodial disposition relative to aggravating and mitigating factors or sentencing objectives cited by judges. Rather, several aggravating and mitigating factors cited by judges, including offence seriousness, prior criminal history, and the plea of the offender were significantly related to sentencing decisions.
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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 | 0.025 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".