A Learning Experience? Enjoyment at Sexual Debut and the Gender Gap in Sexual Desire among Emerging Adults
Bibliographic record
Abstract
Gender differences in experience of first intercourse are among the largest in sexuality research, with women recalling less pleasure and satisfaction than men. This “enjoyment gap” has not been considered in explanations of gender differences in sexual desire. Yet, reinforcement and incentive learning feature prominently in recent models of women’s sexual desire, and nonhuman animal models demonstrate their impact at sexual debut. We examined whether women’s lower sexual desire is explained by their gender or by gendered experience of enjoyment at sexual debut. Emerging adults (N = 838) provided retrospective accounts of physical (orgasm) and affective (satisfaction) enjoyment at (hetero)sexual debut. We replicated gender differences across behavioral, general, and multidimensional measures of trait sexual desire; however, they were contingent on experience and measurement method. When its cognitive multidimensional properties were appreciated, women’s sexual desire varied with experience of orgasm at sexual debut and diverged from men’s only when orgasm did not occur. Such effects were not observed for satisfaction, nor for men. Nor did effects of a control event – masturbatory debut – extend beyond solitary sexual desire. Findings underscore the importance of orgasm equality, and suggest its absence at sexual debut may play an unacknowledged role in differentiating sexual desire.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".