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Record W4410325868 · doi:10.1080/1068316x.2025.2504011

Rape myth acceptance, sexism, and mental representations of women who have experienced sexual assault

2025· article· en· W4410325868 on OpenAlexafffund
Jayme Stewart, Liliana Krank, Leanne ten Brinke

Bibliographic record

VenuePsychology Crime and Law · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSexual Assault and Victimization Studies
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSexual assaultMythologyPsychologyClinical psychologySocial psychologySuicide preventionPoison controlMedicineMedical emergencyHistory

Abstract

fetched live from OpenAlex

Despite being exceedingly common, individuals who have experienced sexual assault are often hesitant to report an assault to friends, family, or police in fear of being disbelieved. People who endorse rape myths and sexist attitudes are less likely to believe disclosures, and survivors who violate stereotypes of how they should look or act are met with skepticism. However, little is known about whether or how stereotypes regarding sexual assault survivors vary from person to person. The current study used a novel technique to generate images that approximate individuals’ mental representations of a woman who disclosed an experience of sexual assault. Results demonstrated that individuals who endorsed more rape myths and sexist attitudes were more likely to stereotype a woman who had experienced a sexual assault as relatively untrustworthy and invulnerable. Findings highlight a novel mechanism for explaining how attitudes might manifest into skeptical responses of survivor disclosures; potential implications for interventions are discussed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.036
GPT teacher head0.419
Teacher spread0.383 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes2
Has abstractyes

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