Reforming Homicide Law to Separate Guilt from Sentence: An International Gloss
Bibliographic record
Abstract
This article argues that Canadian homicide law is handicapped by trying to combine two contradictory approaches. In general, Canadian criminal law adopts the approach of setting out relatively rigid rules for determining guilt or innocence. That is, the Criminal Code sets out particular offences, and if the elements of an offence can be proven, then failing the presence of any defence (also relatively rigidly defined), any accused will be found guilty. The question of guilt or innocence is not individualized to the circumstances of the offender. On the other hand, sentencing decisions adopt exactly the opposite approach, and are made on the assumption that it is necessary to individualize each separate decision.\nBecause first- and second-degree murder have mandatory sentences but the sentencing for manslaughter is flexible, the "guilt or innocence" question is simultaneously a sentencing decision. This approach therefore commits us to doing simultaneously two tasks to which we normally take diametrically opposed approaches. It is no surprise that difficulties should arise.\nDrawing on the experience of a number of other countries with homicide law, defences, and sentencing, the author argues that the relatively simple step of abolishing the mandatory sentence for second-degree murder would resolve a number of inconsistencies and inelegancies in our law.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 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".