The Influence of Sexual Orientation on Attributions of Blame Toward Victims of Sexual Assault
Bibliographic record
Abstract
Previous research has indicated that the sexual orientation of a sexual assault victim influences perceptions of blame. Although researchers have studied perceptions of blame toward straight and lesbian sexual assault victims, none have yet assessed perceptions of blame toward bisexual victims. The purpose of the current study is to examine perceptions of blame toward a female sexual assault victim and to determine whether the victim's sexual orientation impacts the level of attributed blame. Despite no previous research examining the impact of bisexuality on victim blaming, misconceptions that bisexual females are highly sexual, promiscuous, and untrustworthy make it likely that this population would be subject to greater levels of blame than either straight or lesbian victims. After random assignment to a vignette description of a sexual assault and completion of a victim-blaming questionnaire, results failed to support our hypotheses; participants did not blame the bisexual victim of sexual assault more than either the straight or lesbian victims. Further contrary to expected findings, males did not hold more blaming attitudes than females across conditions. Nonetheless, this study is important given the finding that attributions of blame influence whether sexual assault victims choose to disclose their assault, the failure of which can lead to negative mental health outcomes. Ultimately, the current study was a first step in understanding whether bisexual assault victims are evaluated differently than their straight and lesbian counterparts.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".