The sexual responses of sexual sadists.
Bibliographic record
Abstract
On average, rapists show greater relative genital responses to rape stories than do nonrapists in the laboratory. It has been suggested that this robust group difference is explained by the fact that many rapists are sexually sadistic. It is not clear, however, what the critical cues underlying rapists' genital responses are, because rape stories used in previous research include a mix of sadistic cues of violence and victim injury as well as cues of victim resistance and nonconsent. The present study was conducted to identify the critical cues producing self-identified sadists' sexual responses, and thereby to test sexual sadism as an explanation of rapists' arousal pattern. The present study was also conducted to develop a new phallometric test for sexual sadism for research and clinical applications, given evidence of poor diagnostic reliability and validity. Eighteen self-identified male sadists, 22 men with some sadistic interests who did not meet all of our sadist criteria, and 23 nonsadists (all recruited from the community) were compared in their genital and subjective responses to a new set of stories that disentangle violence/injury cues from resistance/nonconsent cues. The three groups differed in both their genital and subjective responses: using indices of relative responding, sadists responded significantly more to cues of violence/injury than nonsadists and men with some sadistic interests. The group difference for cues of nonconsent was not significant. The results suggest that sexual sadism primarily involves arousal to violence/injury in a sexual context rather than resistance/nonconsent.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".