Bibliographic record
Abstract
We support the observation of Holoyda and Newman that common definitions of zoophilia are confusing and that legal definitions of bestiality and sentencing implications are inconsistent. We take issue with their contention that the finding of a history of sex with animals may be a significant risk factor for future harm to humans. We oppose their recommendation for new laws against bestiality to improve psychiatric knowledge about zoophilia. Instead, we advocate for better diagnostic criteria than are provided by the DSM-5, together with the provision of treatment to promote healthful sexual interests and activities by humans and the safety of animals. We believe this is best accomplished by not treating sexual interactions with animals simply as risk factors. Instead they should be assessed as signs of zoophilia, which is a psychiatric disorder for which treatment is available.
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 | 0.007 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.109 | 0.102 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".