Should Hate Speech be Criminalized? Lessons from the Canadian experience in R v. Zundel and R v. Keegstra
Bibliographic record
Abstract
There is a global trend toward increased use of criminal law to combat hate speech. In assessing this trend, one should be mindful of the experience of countries that have long had criminal laws targeting harmful expression. Canada is one such country. Using the leading Canadian cases of R v. Zundel and R v. Keegstra, this article argues that the Canadian experience suggests the criminal law is a flawed mechanism for countering harmful expression. This is so for at least three reasons. First, hate-speech prosecutions may undermine the group dignity and sense of inclusion of minority groups. Second, criminal laws against hate speech do not serve the primary objectives of criminal justice. Third, hate-speech trials may impede the search for truth, a fundamental purpose of the criminal process.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.034 | 0.015 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.012 |
| 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".