Agreement of Self-Reported and Genital Measures of Sexual Arousal in Men and Women: A Meta-Analysis
Bibliographic record
Abstract
The assessment of sexual arousal in men and women informs theoretical studies of human sexuality and provides a method to assess and evaluate the treatment of sexual dysfunctions and paraphilias. Understanding measures of arousal is, therefore, paramount to further theoretical and practical advances in the study of human sexuality. In this meta-analysis, we review research to quantify the extent of agreement between self-reported and genital measures of sexual arousal, to determine if there is a gender difference in this agreement, and to identify theoretical and methodological moderators of subjective-genital agreement. We identified 132 peer- or academically-reviewed laboratory studies published between 1969 and 2007 reporting a correlation between self-reported and genital measures of sexual arousal, with total sample sizes of 2,505 women and 1,918 men. There was a statistically significant gender difference in the agreement between self-reported and genital measures, with men (r = .66) showing a greater degree of agreement than women (r = .26). Two methodological moderators of the gender difference in subjective-genital agreement were identified: stimulus variability and timing of the assessment of self-reported sexual arousal. The results have implications for assessment of sexual arousal, the nature of gender differences in sexual arousal, and models of sexual response.
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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 | 0.028 | 0.068 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".