The Process is the Punishment: Criminal Libel and Political Speech in Canada
Bibliographic record
Abstract
The number of U.S. states with criminal libel laws has been steadily declining since the 1960s, and the offense has been struck down in the United Kingdom and several former British colonies. In Canada, however, criminal libel not only continues to exist, but appears to be enjoying a resurgence, albeit one that has flown beneath the radar of Canadian lawyers, judges, and journalists, who frequently assert that criminal libel prosecutions are rare. The research reported in this article found more than 400 prosecutions since the beginning of the twenty-first century. While many of these cases were brought against people who disseminated sexual slurs against former romantic partners, in a substantial number of cases criminal libel law was used to punish citizens’ political speech, particularly speech critical of police or other justice system officials, a use wholly inconsistent with the Canadian Charter of Rights and Freedoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".