Putting Faith in Hate: When Religion Is the Source or Target of Hate Speech
Bibliographic record
Abstract
To allow or restrict hate speech is a hotly debated issue in many societies. While the right to freedom of speech is fundamental to liberal democracies, most countries have accepted that hate speech causes significant harm and ought to be regulated. Richard Moon examines the application of hate speech laws when religion is either the source or target of such speech. Moon describes the various legal restrictions on hate speech, religious insult, and blasphemy in Canada, Europe and elsewhere, and uses cases from different jurisdictions to illustrate the particular challenges raised by religious hate speech. The issues addressed are highly topical: speech that attacks religious communities, specifically anti-Muslim rhetoric, and hateful speech that is based on religious doctrine or scripture, such as anti-gay speech. The book draws on a rich understanding of freedom of expression, the harms of hate speech, and the role of religion in public life
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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 | 0.001 | 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 | 0.000 | 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) | 0.002 | 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".