Female Multiple Orgasm: An Exploratory Internet-Based Survey
Bibliographic record
Abstract
Women’s multiorgasmic capacity has long been mentioned in the human sexuality literature. However, due in part to the conceptual vagueness surrounding this phenomenon, few empirical studies have focused on this topic, and our scientific knowledge is currently limited. This exploratory research is mainly aimed at providing a much-needed assessment of the profiles of women reporting multiorgasmic experiences. For this study, 419 sexually diverse women ages 18 through 69 who identified as multiorgasmic completed an online survey assessing variables pertaining to sociodemographic background, context and characteristics of a recent/typical multiorgasmic experience, relationships between multiple orgasm and sexual/nonsexual aspects of life, and sexual and orgasmic history. Data reduction analyses using principal component analysis pointed out that 15 variables of interest were distributed across six components, accounting for a large proportion of the sample’s variance. A k-means cluster analysis further revealed that four distinct groups of women could be parsed out. These four groups could be differentiated by three sets of variables—sexual motivation, sexual history, and multiple orgasm characteristics—suggesting that female multiple orgasm is not a unitary phenomenon. This research provides to date the most comprehensive picture of female multiple orgasm and helps refine our conceptual understanding.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".