Pornography Use, Offense-Supportive Cognitions, Atypical Sexual Interests, and Sexual Offending against Children
Bibliographic record
Abstract
The effects of pornography are hotly debated in the scientific literature, especially its potentially negative influence on sexual offending. While studies do not support direct effects of pornography use on sexual offending, pornography is hypothesized to have a catalytic effect among men who are predisposed to sexually offend due to the presence of other risk factors. Using a sample of 241 men varying in sexual offending history, this study examined the associations of different types of pornography consumption, offense-supportive cognitions, and atypical sexual interests on sexual offending against boys or girls. Bivariate analyses support the idea that people seek pornography that matches their sexual interests. Multivariate analyses revealed that the only type of pornography associated with contact sexual offending against boys or girls was child pornography, as it had incremental validity predicting number of victims beyond offense-supportive cognitions and atypical sexual interests. Moderation analyses suggested that, as hypothesized, child pornography consumption amplifies the effect of sexual interests for boys on sexual offending against boy victims. However, that moderation effect was not found for sexual offending against girls. Clinical and research implications are discussed, focusing on the importance of including measures of pornography consumption within forensic assessment and management protocols.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".