Individual Differences in Masturbation and Erotica Consumption Predicting Levels of Sexual Concordance
Bibliographic record
Abstract
Concordance is not the only aspect of sexuality where significant gender differences are observable: men masturbate significantly more than women (Oliver & Hyde, 1993; Petersen & Hyde, 2011). There are also large gender differences in pornography consumption and consumption patterns (Hald, 2006). The study of concordance is important as it could assist in the further development of models of sexual response, and potentially reveal the role of gender differences in those models. Evidence suggests that the cognitive system one uses to process stimuli can affect one's subjective sexual arousal (Dove & Wiederman, 2000). Greater erotica consumption habits could lead to a better familiarity with the erotic stimuli used during the testing protocol, and this decreased novelty could produce more accurate responses for subjective sexual arousal. Using a "bottom-up" cognitive model in which people use physical sensations to infer emotional states, it is likely that increased sexual experience will lead to higher levels of concordance. Opposite-sex attracted participants (24 men and 25 women) will view a series of audiovisual stimuli depicting heterosexual sexual acts and neutral subjects. Participants will answer a series of questionnaires about their sexual history and attitudes, and will answer questions on their level of sexual arousal before and after each stimulus. Participants will continuously report their levels of subjective sexual arousal while simultaneously their genital responses, heart rate and skin conductance will be recorded. It is important to further our understanding of how much impact a participant's previous exposure to erotica, and masturbation behaviours to that erotica, have on their concordance rates; given the increasing pervasiveness and accessibility of erotica, this may prove extremely relevant to future nvestigations
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".