Bibliographic record
Abstract
While the Supreme Court of Canada has been a strong defender of the Gladue sentencing framework that it constructed under section 718.2(e) of the Criminal Code, the Court has so far declined to hold directly that the Gladue approach is constitutionally required. A majority decision in Sharma rejected one possible path to the constitutionalization of Gladue by way of section 15 of the Charter. But the Gladue principles have had a significant impact on the section 12 context, where the integration of Gladue into section 12 jurisprudence reveals much about how Gladue works in the everyday world of sentencing courts. Gladue fits well with section 12 because both protect and tolerate a highly discretionary system of sentencing law in which trial judges must be substantially free to respond to the mix of factors before them. In this system, Gladue functions as an epistemic demand but not as an exceptional law of sentencing for Indigenous people. While the Court has repeated that section 718.2(e) is aimed at reducing overrepresentation of Indigenous people in prison, it is simultaneously clear that judges must apply the universal, fundamental principle of proportionality when imposing sentences on Indigenous people. On two specific topics explored in this paper — the impact of imprisonment and the use of personal traits in reasonable hypotheticals — the Gladue approach has become highly consequential to the question of gross disproportionality under section 12 and, more generally, to the viability of mandatory minimums.
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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 | 0.024 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.098 |
| Scholarly communication | 0.019 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.009 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".