Relations Between Rape Myths, Ambivalent Sexism, Social Dominance Orientation, and Right-Wing Authoritarianism Across Gay and Straight Women and Men: More Similar Than Dissimilar
Bibliographic record
Abstract
Rape myths support and fuel cultural understandings regarding gender roles and deny the victim's rights and strengthen those of the instigator. Little research exists examining the invariance of rape myths measures and models used to explain rape myths across gay and straight samples. Examining correlates of rape myths and determining if the pattern of relations between correlates is similar across gay and straight male and female samples provides insights into socially constant factors that are influencing rape myth acceptance. Participants (294 straight women, 282 gay women, 293 straight men, and 234 gay men) were asked to complete measures of social dominance orientation (SDO), right-wing authoritarianism (RWA), ambivalent sexism toward women, ambivalent sexism toward men, and rape myths toward women. We tested four models that highlighted significant, direct paths between SDO, RWA, and rape myth acceptance. Both hostile sexism toward women and benevolent sexism toward men demonstrated significant indirect effects between SDO, RWA, and rape myth acceptance. Benevolent sexism toward women and hostile sexism toward men demonstrated, in most samples, significant indirect effects between SDO, RWA, and rape myth acceptance. However, the strength of those relations differed for gay and heterosexual samples. This provides further understanding of rape myths as SDO, RWA, and benevolent and hostile sexism toward men and women play a role in supporting rape myth acceptance and establishes that, overall, these relations are more similar than dissimilar across straight and gay samples.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".