Proportionality As a Moral Process: Reconceiving Judicial Discretion and Mandatory Minimum Penalties
Bibliographic record
Abstract
This article reconceives proportionality in sentencing as a constructive reasoning process rather than as an instrumental means of achieving a fair quantum of punishment. It argues that the Supreme Court of Canada has wrongly adopted the latter view by determining the constitutionality of mandatory minimum sentences according to hypothetical outcomes. R v. Nur is a paradigmatic example of how this error presumes a false objectivity in proportionality assessments that leaves the Court vulnerable to critiques of judicial activism. This paper claims that a process-based conception of proportionality offers a stronger defence of judicial discretion in sentencing than the current framework offers; it better respects institutional roles and provides a more principled basis for declaring the current structure of mandatory minimum penalties unconstitutional. The proportionality as a process theory contends that judges alone are capable of reconciling the values of three constituencies in sentencing—the offender, the judge, and the public—and that this tripartite justification is integral to moral punishment. This paper shows how the process view of proportionality in sentencing is an implicit, but under-theorized, current in the law that should be explicitly developed as part of Canadian constitutional theory.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| 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".