Does rape proclivity predict sexual violence? Predictive validity of two distinct rape proclivity measures
Bibliographic record
Abstract
In order to address the majority of individuals who commit sexual violence but are not apprehended, scholars have developed methods to estimate the likelihood of engaging in such behaviour (e.g. Blake & Gannon [2010]. Theimplicit theories of rape-prone men: An information-processinginvestigation. International Journal of Offender Therapy and Comparative Criminology, 54(6), 895–914; Bohner et al. [1998]. Rape myths as neutralising cognitions: evidence for a causalimpact of anti-victim attitudes on men’s self-reported likelihood of raping. European Journal of Social Psychology, 28(2), 257–268). Rape proclivity, a construct that may play an important role in sexual violence, is defined as a self-reported tendency to use sexual aggression. However, little is known about its role in actual perpetration. This study aims to address this gap by examining two different rape proclivity measures as predictors of self-reported aggression. The sample included 389 male undergraduate students from Ontario and 228 community men from North America. Results showed that proclivity measured by the Sexual Experience Survey–Tactics First Revised (SES-TFR) predicted future sexual violence, while the Rape Proclivity Measure did not. This suggests that rape proclivity may be linked to perpetration, but measurement choice is critical.PRACTICAL IMPACT STATEMENT This article will assist researchers and practitioners in designing strategies for prevention of sexual violence among university students and community men by improving understanding of rape proclivity as a construct related to perpetration of sexual violence.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".