<i>Bissonnette</i>: Another Step Forward, After the Harper Decade of Regression in Sentencing
Bibliographic record
Abstract
In R. v. Bissonnette,' the Supreme Court of Canada has provided many reassuring pronouncements in its decision that s. 745.51 of the Criminal Code "is contrary to s.12 of the Charter and not saved under s.1" (para. 4). In cases involving multiple murders, the Court has rejected "the imposition of consecutive parole ineligibility periods" (para. 3) that could readily exceed life expectancy, "a sentence so absurd that it would bring the administration of justice into disrepute" (para. 7). It has strengthened Canadians' protection against any cruel and unusual treatment or punishment with its two-pronged approach, prohibiting "punishment that is grossly disproportionate in relation to the situation of a particular of-fender," but also banning "punishments that, by their very nature, are intrinsically incompatible with human dignity" (para. 6). However, it is neither churlish nor pessimistic to remain guarded about the future of sentencing for this small category of offenders and about the trajectory of punishment more generally, so this comment will canvass several issues which moderate any sense of relief after the striking down of s. 745.51.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.014 | 0.013 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".