Lost in translation: a quantitative and qualitative comparison of rape myth acceptance
Bibliographic record
Abstract
Rape myths (RMs) are a complex set of cultural beliefs and attitudes that support and condone sexual violence, mainly by shifting blame from the perpetrator to the victim. Much empirical attention has been paid to how RMs perpetuate cultural norms that justify sexually assaultive behaviours, with research demonstrating that individuals who have higher rape myth acceptance (RMA) are less likely to believe victims of sexual assault, report their own assault if victimized, and are themselves at an increased risk for sexual violence perpetration. Though several methods exist for assessing RMA, shifting cultural norms make it increasingly difficult to accurately assess RMA using traditional quantitative methods; existing research shows discrepancies in response patterns between qualitative and quantitative examinations of RMAs. In a mock-jury paradigm, university (n = 86) and community-based participants (n = 82) responded to a fictitious police report of sexual coercion between two romantic partners. Results indicated that although respondents endorsed low levels of RMA on a self-report measure (updated IRMA), their qualitative responses endorsed four distinct RMs, such as “she asked for it,” which attributes responsibility for the assault to the victim. Implications and future directions for research will be 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 | 0.034 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".