Bibliographic record
Abstract
Abstract A commitment to free speech is a fundamental precept of all liberal democracies. However, democracies differ significantly when addressing the permissibility of laws regulating certain kinds of speech, especially extreme speech. In the United States, for instance, the commitment to free speech has been held by the Supreme Court to protect the public expression of even the most noxious racist ideology. In contrast, in almost every other democracy governments enjoy considerable leeway to restrict racist and other types of extreme expression. What accounts for the marked differences in attitude towards the constitutionality of hate speech regulation? Does hate speech regulation violate the core free speech principle constitutive of democracy? Or do values such as the commitment to equality or individual dignity legitimately override the right to free speech in some circumstances? In attempting to answer these and other questions, this book focuses on highly topical issues such as homophobic speech, Holocaust denial, incitement to terrorism, veiling controversies, and the Danish cartoons depicting the Prophet Muhammad. It includes interdisciplinary perspectives from law, philosophy, history, psychology, and literature, and provides comparative perspectives from experts in various countries including Australia, Canada, France, Germany, Hungary, and Israel, as well as from the United States and the United Kingdom.
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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 | 0.000 | 0.001 |
| 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 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".