The development and validation of the motives for feigning orgasms scale
Bibliographic record
Abstract
Most research on feigning orgasm has focused exclusively on women and on potential predictors of this behaviour, with little attention given to the underlying motives for doing so. There are currently no available scales measuring individuals' motives for feigning orgasm. The purpose of the current research was to develop and validate a scale to assess motives for feigning orgasm among men and women. In Study 1, 53 men and 94 women completed a preliminary version of the Motives for Feigning Orgasms Scale (MFOS). More women (43.1%) than men (17.3%) indicated that that they had pretended to have an orgasm with their current relationship partner. Factor analysis was performed, yielding a six-factor solution (i.e., Intoxication, Partner Self-Esteem, Poor Sex/Partner, Desireless Sex, Timing, and Insecurity). In Study 2, the MFOS was completed by 194 participants. Confirmatory factor analysis was conducted; however this analysis supported three models (i.e., two two-factor models, and one three-factor model). The Sexual Goals Questionnaire, the Behavioural Inhibition System/Behavioural Activation System Scale, and the Sexual Compulsivity Scale were also completed concurrently with the MFOS, and yielded results that supported the MFOS's convergent and discriminant validity. Men were more likely than women to report pretending orgasm due to intoxication, discomfort or displeasure attributable to the sexual experience or to their sexual partner, and feelings of insecurity. No other gender differences on the MFOS's subscales were found. The MFOS is a new comprehensive measure of individuals' motivations for feigning orgasm that can help enhance our understanding of human sexual motivation.
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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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".