T11-O-08 Bisexual women differ from lesbian and heterosexual women on several sexuality measures
Bibliographic record
Abstract
Compare sexual orientation groups on: numbers of male and female sexual partners and total numbers of partners in lifetime and the past year; propensity for casual sex; frequency of condom use in the past year; erotophilia-erotophobia; sexual sensation seeking; propensity for sexual excitation and sexual inhibition. A volunteer sample of 545 women from the USA and Canada completed anonymous questionnaires. Analyses were conducted based on two groupings: self-identity [heterosexual n=450, bisexual n=46, lesbian n=49] and lifetime partner type [women who had sex with men only (WSMO) n=396, women who had sex with men and women (WSMW) n=136, women who had sex with women only (WSWO) n=13]. Comparison of the scores across sexual orientation groups included age as a covariate in a series of univariate GLM analyses. Statistically significant sexual orientation group differences (based on both self-identity and lifetime partner type) were found for almost all variables. These were not explained by other demographic differences. Specifically, bisexuals showed a different pattern of responding than both heterosexual and lesbian women, and WSMW scored differently than WSMO. WSWO did not differ from WSMW or WSMO. Research merging bisexuals with lesbians (or with heterosexuals) OR merging WSMW with WSMO as WSM may obscure important differences relevant to sexuality and sexual health. Care should be exercised in conceptualizing bisexuality as “in-between” heterosexuality and homosexuality. Attention should be given to discordant findings across multiple dimensions of sexual orientation and various aspects of sexuality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".