Bibliographic record
Abstract
Abstract Purpose – This chapter has three general purposes: to trace Canada’s hate speech laws from their policy inception to their current state; to identify the importance that media and mass communication have played in the creation and development of Canada’s hate speech laws; and to demonstrate the critical relationship that media has had to significant legal cases on hate speech. Methodology/Approach – This chapter historically maps the policy development of and legal challenges to Canada’s hate speech laws. It takes directed notice of the relationship of media and mass communication to the development and implementation of those laws. It engages with libertarian and egalitarian arguments on free speech throughout the chapter testing these ideas through an examination of the legal cases cited. Findings – Canadian legislators and courts have long grappled with the balancing of rights with respect to the issue of “hate speech.” Advances in mass communication technology have added intricate challenges to that legal balancing. Awareness of media’s allure to hatemongers and racial extremists and of media’s protean characteristics make regulation of its hateful content a continuous legal challenge. Canada’s greatest challenge yet to the regulation of hate speech will be its adaptive response to the growing phenomenon of online hate. Originality/Value – This chapter highlights the little recognized prescient statements made by the Cohen Committee about the allure of media and the dangers of its technological advancements in Canadian free speech debates. Providing a comprehensive survey of Canada’s “hate speech” laws, it recognizes the importance that advancements in mass communication have played in the creation and development of Canada’s “hate speech” laws.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 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".