Gender-specific genital and subjective sexual arousal to prepotent sexual stimuli in androphilic men and gynephilic women
Bibliographic record
Abstract
Marked differences have been found in men’s and women’s sexual response patterns, contingent upon their sexual orientation; androphilic (attracted to men) and gynephilic (attracted to women) men demonstrate greatest genital and self-reported arousal to their preferred stimulus type (a “gender-specific” response), whereas androphilic women do not, and findings for gynephilic women have been mixed. While there have been many investigations into gynephilic men’s and androphilic women’s (i.e., heterosexual men/women) sexual response, there has been less investigation into the specificity of sexual response of androphilic men and gynephilic women. Given the complex nature of sexual stimuli that are used in sexual response research, it is often unclear to what extent contextual cues (e.g., cues other than the sexual actor’s primary and secondary sex characteristics, such as physical attractiveness, sexual activity, etc.) influence participants’ sexual response patterns. As such, the current study examined genital, discrete self-reported, and continuous self-reported responses of androphilic men ( n = 22) and gynephilic women ( n = 10) to prepotent sexual features (stimuli thought to elicit automatic sexual arousal: erect penises and exposed vulvas), non-prepotent sexual features (flaccid penises and pubic triangles) and neutral stimuli (clothed men and women). Both samples exhibited a gender-specific pattern of genital, self-reported, and continuous self-reported sexual arousal. Similarly, all measures of sexual arousal were generally found to be greatest to “prepotent” sexual cues. Implications for understanding gender specificity of sexual response are discussed.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".