Judicial Discretion and the Rise of Individualization: The Canadian Sentencing Approach
Bibliographic record
Abstract
“Who are courts sentencing if not the offender standing in front of them?”\nThe epigraph to this paper points to the ethical heart of a distinctive and important development in Canadian sentencing law. It is drawn from a case in which the Supreme Court of Canada grappled with the signal societal trauma wrought by the operation of the criminal justice system – the travesty of Indigenous over-representation in Canadian prisons. This development involves an approach that has already disrupted certain elements of contemporary sentencing practice in Canada, and it is one that, depending on how sentencing judges embrace it, may open up new futures in Canadian sentencing. This development is the emergence of individualized proportionality as the fundamental principle of sentencing in Canada. One object of this paper is to explain and explore the rise, shape, and implications of this deep commitment to individualization as the defining feature of contemporary Canadian sentencing law.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.028 | 0.039 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".