What Exactly Is an Unusual Sexual Fantasy?
Bibliographic record
Abstract
INTRODUCTION: Although several theories and treatment plans use unusual sexual fantasies (SF) as a way to identify deviancy, they seldom describe how the fantasies referred to were determined to be unusual. AIM: The main goal of this study was to determine which SF are rare, unusual, common, or typical from a statistical point of view among a relatively large sample of adults recruited from the general population. A secondary goal was to provide a statistical comparison of the nature and intensity of sexual fantasies for men and women. This study also aims at demonstrating with both quantitative and qualitative analyses that certain fantasies often considered to be unusual are common. METHODS: An Internet survey was conducted with 1,516 adults (799 ♀; 717 ♂) who ranked 55 different SF and wrote their own favorite SF. Each SF was rated as statistically rare (2.3% or less), unusual (15.9% or less), common (more than 50%), or typical (more than 84.1% of the sample). MAIN OUTCOME MEASURES: An extended version of the Wilson's Sex Fantasy Questionnaire with an open question. RESULTS: Only two sexual fantasies were found to be rare for women or men, while nine others were unusual. Thirty sexual fantasies were common for one or both genders, and only five were typical. These results were confirmed with qualitative analyses. Submission and domination themes were not only common for both men and women, but they were also significantly related to each other. Moreover, the presence of a single submissive fantasy was a significant predictor of overall scores for all SF in both genders. CONCLUSION: Care should be taken before labeling an SF as unusual, let alone deviant. It suggested that the focus should be on the effect of a sexual fantasy rather than its content.
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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 | 0.009 |
| 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 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".