Asserting sexual (dis)interest: How do women’s capabilities differ?
Bibliographic record
Abstract
Sexual autonomy implies consistency between one’s internal desires and sexual behaviours. Sexual assertiveness has been defined as the strategies used to accomplish such consistency, and to therefore be sexually autonomous. Sexual assertiveness encompasses skills in refusing unwanted sexual situations and bringing about wanted sexual situations. Measures of sexual assertiveness typically assess both refusal sexual assertiveness (RSA) and initiation sexual assertiveness (ISA), yet there is a dearth of research examining these skills in relation to one another. The present study examined the relationship between women’s RSA and ISA, exploring predictors of each. A total of 487 heterosexual and 129 lesbian, bisexual, questioning and other (LBQ +) women completed the online survey, including women recruited from an undergraduate psychology program at an Ontario university and from communities across Canada using social media. ISA and RSA were only moderately correlated. General assertiveness in non-sexual situations was only one of several variables predictive of ISA and RSA, indicating that there is something unique to assertiveness in the sexual context. Committed relationship context and erotophilic disposition specifically predicted initiation assertiveness. Less endorsement of the sexual double standard specifically predicted refusal assertiveness. No significant differences emerged in predictors of ISA and RSA when comparing sexual orientation groups. However, LBQ + women unexpectedly reported lower levels of RSA overall. Implications for supporting the development of sexual assertiveness and avenues for future research are discussed.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".