The impact of empathy priming on the Rape Myth Acceptance Scale for those with antisocial/psychopathic traits
Bibliographic record
Abstract
Rape Myth Acceptance (RMA) remains a persistent problem in rape and sexual assault cases. Several scholars (e.g. Mouilso, E. R., & Calhoun, K. S. (2013). The role of rape myth acceptance and psychopathy in sexual assault perpetration. Journal of Aggression, Maltreatment, and Trauma, 22(2), 159–174. https://doi.org/10.1080/10926771.2013.743937) discussed RMA as a cognitive distortion that constitutes a crucial link between psychopathy and rape perpetration. Research has indicated that empathy can help counteract bias and promote understanding and support for individuals who have experienced sexual violence (Batson, C. D., Polycarpou, M. P., Harmon-Jones, E., Imhoff, H. J., Mitchener, E. C., Bednar, L. L., Klein, T. R., & Highberger, L. (1997). Empathy and attitudes: Can feeling for a member of a stigmatized group improve feelings toward the group? Journal of Personality and Social Psychology, 72(1), 105–118. https://doi.org/10.1037/0022-3514.72.1.105). The overall goal of this study was to experimentally assess the relationship between empathy priming, psychopathy, and RMA. Specifically, it aimed to examine whether psychopathic traits influence the relationship between empathy priming and RMA. The study included 518 participants from Ontario Tech University and the community. Results indicated that the amount of effort invested in the empathy priming task significantly reduced rape-supportive attitudes, but only when the participants had high levels of psychopathic personality traits. Limitations of the study and potential directions for future research are discussed.PRACTICE IMPACT STATEMENT The present research offers a unique perspective in understanding the efficiency of empathy priming on rape-supportive attitudes in individuals with high- and low-trait psychopathy. Additionally, it highlights the significance of active involvement and engagement in empathy-based sexual assault prevention programmes, particularly when targeting individuals with high psychopathic traits.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".