Free Speech & its Limits: A Study of the Rippling Effects of Hate Speech Laws in Canada
Bibliographic record
Abstract
This dissertation critically examines the loopholes in Canada’s hate speech legislation and its adjudication processes within courts and tribunals. It argues that Canadian hate speech laws are founded on expansive notions of harm, creating a slippery slope where protected expressions can also face restrictions. This dissertation argues that the current hate speech legal framework in Canada overlooks speech as an exceptional social phenomenon that is inextricable from human creativity, which is inherently polysemous, versatile, and interpretive, especially concerning sociopolitical, ideological, and cultural viewpoints. The core argument of this dissertation is that given the characteristics and complexities of speech and the lack of evidence that can link an alleged hate speech to its harm, hate speech cases are adjudicated through a common sense or deference to legislative judgment approach, and not through deductive and evidence-based reasoning. By closely analyzing hate speech cases, this dissertation demonstrates that in Canada the adjudication of hate speech cases is excessively subjective and inconsistent. This dissertation examines the rippling effects of Canada’s hate speech legal regime by uncovering the intertwining of hate speech laws with politics, leading to the rise of a phenomenon termed ‘speech scare’ that imposes societal and cultural pressures on free expression, especially on controversial topics. Finally, the dissertation examines the discourse of online hate speech, revealing how excessive pressure for online communication moderation can have more detrimental effects on the right to freedom of expression and the right to privacy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.041 | 0.019 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".