Sentencing aboriginal offenders: section 718.2(e) of the Criminal Code of Canada and aboriginal over-representation in Canadian prisons
Bibliographic record
Abstract
Although enacted in 1996 with the intent of ameliorating the social problem of Aboriginal over-representation in Canadian prisons, section 718.2(e) of the Criminal Code has been largely ineffective in achieving its goal. To the surprise of many, Aboriginal over-incarceration levels have actually increased since the provision's enactment over six years ago. This thesis explores how section 718.2(e) has been applied in sentencing cases that have involved Aboriginal offenders, since the enactment of the provision in 1996 to the present, in an effort to identify possible reasons as to why Aboriginal over-incarceration levels have failed to decrease. One hundred and seventyseven Canadian criminal cases involving Aboriginal offenders and section 718.2(e) are examined. In addition, materials gathered in interviews with seven Provincial Court judges are discussed. The main finding of this study is that, although sentencing judges are applying section 7 l8.2(e) to justify mitigated sentences in most cases, the majority of the sentences handed out were nevertheless carceral. The data also show that noncarceral terms could not be justified in the majority of those cases, as the offenders had committed serious offences and had long and/or serious prior records. The author argues that the primary reason why the provision fails its mandate is because it does not address the root causes of Aboriginal over-incarceration. The author concludes that, since Aboriginal over-incarceration is a symptom of a larger problem that stems from social, political, and economic issues, efforts should be directed at the social, economic and political realms to address the roots of the problem. By addressing the disadvantage that causes criminality in the first place, the carceral levels associated with that criminality should decrease, and the problem of Aboriginal over-incarceration should be solved.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".