From Gross Disproportionality to Human Dignity: Redefining Section 12 in the Context of Mandatory Minimum Sentences
Bibliographic record
Abstract
In this short paper, we contend that human dignity must remain at the heart of the section 12 analysis, and should be strengthened in future cases that will be tackling the remaining mandatory minimums. In so doing, we critically examine the Supreme Court’s recent development of the section 12 framework, particularly in light of the 2023 mandatory minimum jurisprudence, with a specific emphasis on the concept of human dignity. The analysis centers on the role of reasonable hypotheticals in advancing the primary objective of section 12 — safeguarding human dignity — by scrutinizing the three pivotal components of the gross disproportionality analysis. We suggest that the section 12 analysis focuses on proportionality and human dignity rather than the current “gross disproportionality” standard in assessing what constitutes cruel and unusual punishment. These suggestions are underpinned by an approach that considers proportionality a principle rooted in human dignity. Nevertheless, proportionality has its limits and therefore section 12 should be complemented by an approach that takes into account prison conditions and the effects of imprisonment on marginalized groups, which are rooted in inequalities. Finally, the analysis questions the third component of the gross disproportionality analysis, which calls for deference to the legislature in order to prioritize objectives that instrumentalize the individual and are contrary to human dignity. These objectives are problematic, we suggest, and best addressed within section 1 of the Charter.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.007 | 0.012 |
| 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".