Religious Vilification: Confused Policy, Unsound Principle and Unfortunate Law
Bibliographic record
Abstract
It would be a very good thing and no doubt society would be better for it, if certain benighted people refrained from insulting or denigrating their fellow citizens because of those citizens' religious beliefs and conduct. Should we then pass a law to prohibit religious vilification? In this article I argue that a firm 'no' should be the answer. I realise that in some quarters the subject has been thoroughly debated and the opposite answer given. So, the United Kingdom,1 as well as three states in Australia, have recently enacted laws banning incitement to religious hatred. The question, however, is still a live one for nations such as New Zealand and Canada, as well as the remaining states of Australia. Moreover, even in those jurisdictions saddled with such laws, it is not too late to reconsider and scrap the legislation. The justifications for the introduction of religious vilification laws have never been persuasive. Whilst I shall briefly traverse these, the best argument against religious vilification is, I believe, the Catch the Fire case. This decision, the first major litigation on the subject, bears out the concerns of many that religious vilification laws are conceptually unsound and produce results antithetical to the religious tolerance its promoters hope for.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.027 | 0.023 |
| Insufficient payload (model declined to judge) | 0.004 | 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".