Hierarchies of Harm in Canadian Criminal Law: The Marijuana Trilogy and the Forcible “Correction” of Children
Bibliographic record
Abstract
This paper examines the Supreme Court of Canada’s use of the concept of “harm” in its decisions in R. v. Malmo-Levine, which upholds the criminal prohibition on marijuana possession, and Canadian Foundation for Children, Youth and the Law v. Canada (Attorney General), which upholds the reasonable correction defense for parents and teachers charged with assaults against children in their care. The author argues that these decisions employ contradictory characterizations of the role of harm in determining the proper scope of the criminal law. Although the majority in Malmo-Levine cites protecting vulnerable groups from harm as a valid purpose of the criminal law, the Court fails in Canadian Foundation to adequately consider how the reasonable correction defense in fact perpetuates the harms children suffer as a result of socially accepted physical discipline. Further, the Court suggests in Malmo- Levine that conduct causing only trivial harm within the de minimis range cannot legitimately attract criminal sanction, but the n claims in Canadian Foundation that the reasonable correction defense is necessary to prevent families from being torn apart when parents are charged with assault for trivial touchings. The author supports the Court’s decision in Malmo-Levine, but argues that its reasoning in Canadian Foundation ignores discrimination against a vulnerable group.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.025 | 0.031 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".