Religion, Expression and Freedom: Offense as a Weak Reason for Legal Regulation
Bibliographic record
Abstract
Some forms of religious expression tend to stir controversy among citizens who share a secular conception of public morality, whether or not they are themselves religious in the 'private sphere.' As well, religious individuals are often shocked by expression criticizing their beliefs or desacralizing the religious figures that they worship. This paper examines some difficult questions raised by the interplay of freedom of expression and freedom of religion. Drawing on Raymond Boudon’s distinction between strong and good reasons, it offers a reflection on the quality of reasons invoked in support of claims demanding the censorship of either public manifestations of religious belief or anti-religious expression, observing that several of them, being neither strong nor good, are more often than not rather weak. It defends the thesis that the equilibrium between the two freedoms cannot be judged in the abstract but must instead be resituated within the particular historical and legal context within which it is established. However, it warns against the creation of double standards between religious expression and anti-religious expression, arguing that if the former is strongly protected, the other should be protected as strongly, irrespective of believers’ offended sensitivities. Secular moralities are no more, but no less, important, than religious ones.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.057 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".