Examining the orgasm gap in a diverse sample with mixed methods
Bibliographic record
Abstract
Research has identified gender differences in orgasm frequency during sex between heterosexual individuals. This orgasm gap is reduced for cisgender women who have sex with women, while cisgender men typically report similar orgasm frequency regardless of sexual orientation. Current consensus around reasons for this gap implicates sociocultural factors. For instance, common sexual norms and scripts prioritise cisgender men’s pleasure. The present study used a mixed methods approach to understand the orgasm gap in a sample inclusive of sexually and gender minoritized and racialised individuals. A total of 5423 individuals completed an online survey in September 2020. A factorial ANOVA was used to assess orgasm frequency across minority versus majority groups (based on gender, sexual orientation, partner gender, and race), in the context of sexual activity with a partner. Qualitative content analysis further examined self-reported barriers to orgasm. Findings of the present study generally replicate existing literature on the orgasm gap regarding factors including gender and partner gender. Significant differences in orgasm frequency were not found between majoritized and minoritized races or between heterosexual and minoritized sexual orientations. Qualitative analysis results highlight both personal (including health related) as well as interpersonal contributors to perceived discrepancies in orgasm frequency.
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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 | 0.033 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".