“To attack the rights of one man is to attack the rights of everyone”: Conservative Rights Talk and Canada’s First Hate Speech Laws
Bibliographic record
Abstract
In 1970, the Canadian government amended the Criminal Code to include the nation’s first provisions banning hate propaganda. This article examines the parliamentary debates over these provisions, from their introduction to their adoption, to assess how members of the two mainstream political right and right-of-center parties responded to the proposed amendments to the Criminal Code. It considers what this response—and the way in which it was articulated—reveals about how self-identified conservatives understood rights and freedoms, and how they conceived of the role of the state in securing these rights and freedoms in the context of Canada’s expanding legislative human rights framework. I argue that these debates over hate speech are illustrative of a coherent form of conservative rights-talk in 1960s Canada, one that varied from, and in some respects was in opposition to, a more dominant liberal discourse of rights.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| 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".