Sexual consent: Exploring the perceptions of heterosexual and non-heterosexual men
Bibliographic record
Abstract
The current understanding of sexual consent negotiation is grounded in research conducted with heterosexual populations, and little is understood about how non-heterosexual men (bisexual, bi-curious, two-spirited, other) navigate these processes. A sample of 251 heterosexual men and 313 non-heterosexual men participated in an online survey where they were asked to respond to an open-ended question that addressed their perceptions of the differences between how heterosexual and non-heterosexual men negotiate sexual consent. Participants were recruited through social media (i.e. Facebook, Twitter), Amazon’s Mechanical Turk, and via the distribution of flyers/posters. The sample consisted of men from Canada, the United States, and Western Europe. Basic demographic information was gathered along with self-identified sexual orientation. Four main themes were derived through the thematic analysis of responses:understanding of sexual interactions, understanding of sexual script, unique challenges, and the universality of sexual consent. Findings provide initial insight into some of the perceived differences and barrier both non-heterosexual and heterosexual men face in negotiating sexual consent and highlight some of the entrenched heteronormative beliefs that both heterosexual and non-heterosexual men endorse. Results can serve to inform social interactions, education, and policymaking.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".