Bibliographic record
Abstract
This study aims to gain an understanding of the sexual arousal patterns of women who have some degree of sexual attraction to both males and females. Women’s genital arousal has been found to be nonspecific. That is, women have been found to show physiological arousal to stimuli depicting both their preferred and non-preferred gender (Chivers, Seto, & Blanchard, 2007). This previous research has examined arousal patterns of heterosexual and homosexual females but no research to date has focused on bisexual women. Bisexuality has been found to be more prevalent in women than homosexuality (Mosher, Chandra, & Jones, 2005); therefore, it is important to understand the arousal patterns of these women. In the current study, women’s physiological and self-reported arousal will be measured as they view erotic videos varying by degree of sexual activity (e.g., naked exercising, masturbation, coupled intercourse) and varying by gender of the sexual actors. Based on past literature, I expect to find that both physiological and subjective arousal levels will increase as the level of sexual activity in the videos increases (e.g. greater arousal to intercourse than to masturbation). I also expect to find that the correlation between bisexual women’s physiological and subjective arousal will be stronger than the concordance for either heterosexual or homosexual women. Finally, I expect frequency of masturbation, use of erotica, and sex toy use to be positively correlated with women's sexual arousal concordance (between physiological and subjective arousal).
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".