Bibliographic record
Abstract
Section 718.2(e) of the Criminal Code directs sentencing judges to exercise restraint in the use of incarceration ‘with particular attention to the circumstances of Aboriginal offenders.’ In R v Gladue, the Supreme Court of Canada interpreted this as a remedial provision aiming to reduce the incarceration of Indigenous people. That has made it appear to be a failure by its own lights. Yet to write off section 718.2(e) and the Gladue principles would be to fail properly to understand their moral foundations and structure. Judges are called upon to reduce the incarceration of Indigenous people neither by working backwards from prison demographic targets nor merely by combating implicit bias. Rather, Gladue requires judges to open their minds to hitherto unappreciated reasons that many Indigenous offenders should be afforded mitigation, restorative justice, and community-based accountability. One reason, we argue, relates to the unfair criminogenic disadvantages disproportionately faced by Indigenous offenders. Another reason is that the Canadian state’s complicity in such disadvantages calls into question its legitimate authority and its standing to blame Indigenous offenders. In sum, Gladue calls upon our courts to widen the horizon of fairness in their treatment of Indigenous people. This matters for its own sake in each and every case, whether or not it brings about an appreciable reduction in Indigenous incarceration in the aggregate. Our reconstruction of Gladue not only rescues it from cynical dismissals but also helps to solve the central doctrinal puzzles surrounding it: how Indigenous offenders’ unique life circumstances must be connected to their offences to be mitigating; how Gladue principles should apply differently to more and less serious offences; how a variant of Gladue principles should be extended to members of other disadvantaged groups such as Black Canadians; and how judges should weigh the interests of Indigenous victims when sentencing Indigenous offenders.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.018 | 0.034 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".