Ipeelee and the Pursuit of Proportionality in a World of Mandatory Minimum Sentences
Bibliographic record
Abstract
The law of sentencing in Canada is being pulled in opposing directions: Parliament regularly legislates new mandatory sentences that limit judicial discretion while the Supreme Court strongly affirms the “highly individualized” nature of sentencing. Mandatory sentences have proliferated in recent years, contrary to overwhelming social science evidence that they do not deliver on their promises of deterrence and crime control, and largely unimpeded by the Charter. However, the recent decision in R v Ipeelee arguably puts the principles relevant to the sentencing of Aboriginal people on a collision course with the substantial limits on judicial discretion that are central to mandatory minimum sentences. In this brief article, I first outline some of the key holdings in Ipeelee, arguing for their robust application at a time when judicial discretion in sentencing is being limited. I then move on to discuss the extent to which mandatory sentences have a disproportionate impact on Aboriginal people in a way that should attract Charter scrutiny.
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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 | 0.024 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.005 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".