Tipping the Scales: Effects of Gender, Rape Myth Acceptance, and Anti-Fat Attitudes on Judgments of Sexual Coercion Scenarios
Bibliographic record
Abstract
A damaging belief exists that to become a victim of sexual violence, victims must be deemed sexually desirable. As a result, sexual violations where the victims are individuals whom society may deem as less attractive—such as overweight women—may elicit less empathy for the victim or minimize the likelihood they are believed. Yet, there is some evidence that overweight women actually report higher rates of sexual violence than women of other weight categories. Although there has been some research implicating weight biases in sexual assault cases, this has not been extended to cases of sexual coercion despite their growing share of police reports. A sample of 168 participants were recruited from Canada via social media ( n = 82) and through a midsized university in Ontario, Canada ( n = 86). Using a mock jury paradigm, participants responded to a vignette depicting the sexual coercion of a thin or overweight woman. Participants reported their opinions on the sexual coercion scenario, and prejudicial attitudes, using two standardized scales. Men reported greater rape myth acceptance, anti-fat attitudes, and victim responsibility and endorsed significantly more perpetrator mitigating factors and expressed more negative affect toward the victim. Participants in the overweight condition also expressed greater perpetrator sympathy, greater perpetrator mitigation, and less negative affect toward the perpetrator. These results suggest that overweight women may face additional barriers when reporting their experiences of sexual coercion, particularly to men.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".