Not All Orgasms Were Created Equal: Differences in Frequency and Satisfaction of Orgasm Experiences by Sexual Activity in Same-Sex Versus Mixed-Sex Relationships
Bibliographic record
Abstract
Which sexual activities result in the most frequent and most satisfying orgasms for men and women in same- and mixed-sex relationships? The current study utilized a convenience sample of 806 participants who completed an online survey concerning the types of sexual activities through which they experience orgasms. Participants indicated how frequently they reached orgasm, how satisfied they were from orgasms resulting from 14 sexual activities, and whether they desired a frequency change for each sexual activity. We present the overall levels of satisfaction, frequency, and desired frequency change for the whole sample and also compare responses across four groups of participants: men and women in same-sex relationships and men and women in mixed-sex relationships. While all participants reported engaging in a wide variety of activities that either could, or often did, lead to the experience of orgasm, there were differences in the levels of satisfaction derived from different types of orgasms for different types of participants, who also engaged in such activities with varying degrees of frequency. We discuss group differences within the context of sexual scripts for same- and mixed-sex couples and question the potential explanations for gender differences in the ability to experience orgasm during partnered sexual activity.
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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 | 0.007 |
| 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 | 0.000 |
| 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".