Bibliographic record
Abstract
Laws against sexual obscenity rely on a distinction between explicit materials that merely offend and materials that cause something worse than offense. While most offensive content is protected under the banner of freedom of expression, obscenity is not. In this paper I try to locate a distinctive harm in the case of obscenity, that would justify prohibiting this material while permitting other kinds of offensive content. I argue that the best case for laws against obscenity relies on the concept of moral harm. If we rely on Mill’s Harm Principle and moral harm is a real harm, then it could be used to justify the distinction between protected and unprotected sexually explicit speech. I argue this demonstrates a weakness in the Harm Principle as a liberal principle of justice. By giving weight to moral harm, Mill’s principle risks eroding an important distinction between the public and private domains.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.054 |
| Scholarly communication | 0.008 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".