Observational Stance as a Predictor of Subjective and Genital Sexual Arousal in Men and Women
Bibliographic record
Abstract
Observational stance refers to the perspective a person takes while viewing a sexual stimulus, either as a passive observer (observer stance) or an active participant (participant stance). The objective of the current study was to examine the relationship between observational stance and sexual arousal (subjective and genital) across a range of sexual stimuli that do or do not correspond with a participant's sexual attraction (preferred or nonpreferred stimuli, respectively). Regression analyses revealed that, for men (n = 44), participant stance significantly predicted subjective and genital arousal. Women's (n = 47) observer and participant stance predicted subjective arousal but not genital arousal. Analysis of variance showed that participant stance was greatest under preferred sexual stimuli conditions for all groups of participants, while observer stance scores revealed a less consistent pattern of response. This was particularly true for opposite-sex-attracted women, whose ratings of observer stance were lowest for preferred stimuli. Observational stance does not appear to account for gender differences in specificity of sexual arousal; for men, however, participant stance uniquely predicted genital response after controlling for sexual attractions. Similarities in the relationships between men's and women's observational stance and sexual responses challenge previous claims of gender differences in how men and women view erotica.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".