Not all fakes are created equal: examining the relationships between men's motives for pretending orgasm and levels of sexual desire, and relationship and sexual satisfaction
Bibliographic record
Abstract
Limited research on feigning orgasm, particularly among men, exists, and even less investigates motivations for doing so. Further, whether feigning orgasm, and motivations for feigning orgasm, is associated with sexual and relationship satisfaction and sexual desire is unknown. Thus, the purpose of the current study was to examine these relationships in a sample of 230 men (18–29 years old) having pretended orgasm with their current relationship partner at least once. Participants were recruited on Amazon Mechanical Turk. On average, participants reported feigning orgasm in approximately one-fourth of sexual encounters in their current sexual relationship, most commonly during vaginal sex. Feigning orgasm for reasons related to a poor sexual experience or to poor partner choice was the strongest predictor; associated with lower levels of desire and sexual and relationship satisfaction. Feigning orgasm to support a partner's emotional well-being was associated with higher levels of desire. Feigning orgasm because one was intoxicated, having undesired sex, or out of a desire to improve the quality of the sexual encounter was associated with higher levels of sexual satisfaction (though these variables accounted for little variance). This research indicates men do feign orgasm, and motivations for doing so are associated with sexual and relational outcomes.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".