Principled Justice for Indigenous Peoples? An Empirical Analysis of the Application of Gladue Factors in Canadian Lower Courts
Bibliographic record
Abstract
This article examines section 718.2(e) of the Criminal Code, which is aimed at reducing the imprisonment of Indigenous offenders and the application of the Supreme Court of Canada’s decision in R. v. Gladue. Using court observation of docket court sentencing, this article demonstrates that the restrictive context of the lower court sentencing environment, along with the complexity of the sentencing task, influences and reduces the number and kinds of Gladue sentencing factors considered and restricts a judge’s ability to appropriately weigh the factors that ought to play a meaningful role in Indigenous sentencing. Drawing from research literature from the fields of behavioural economics and psychology about cognitive bias, heuristics, and the use of stereotypes, the findings of this study suggest that, faced with these circumstances, judges may rely on heuristics and form judgments about a defendant’s character and their potential future behaviour. In this way, stereotypes relating to offenders’ race permeate their sentencing decisions. The findings that Indigenous sentencing principles are not being employed in a principled way have important implications for the legitimacy of our legal institutions.
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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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".