R. v. Ferguson and the Search for a Coherent Approach to Mandatory Minimum Sentences under Section 12
Bibliographic record
Abstract
Since the early days of the Charter, uncertainty prevailed about constitutional exemptions as a remedy for breaches of the section 12 guarantee against “cruel and unusual treatment or punishment”. It was unclear whether an offender could be exempted from the application of a mandatory minimum sentence that would produce an unconstitutional result in the unique circumstances of the case. The Supreme Court of Canada recently decided this issue, ruling in R. v. Ferguson that constitutional exemptions are unavailable under section 12. However, the author argues that uncertainty lingers in the wake of Ferguson because the Supreme Court failed to resolve the underlying issue, which is how to address sentencing provisions that operate constitutionally in most cases but have unconstitutional effects in rare cases. Viewed in its jurisprudential context, Ferguson suggests that section 12 provides little protection to individuals whose exceptional circumstances render the application of a mandatory minimum sentence cruel and unusual.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.043 | 0.024 |
| Insufficient payload (model declined to judge) | 0.002 | 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".