Bibliographic record
Abstract
Research has demonstrated that psychopathic traits are associated with risk taking spread across a variety of domains. One domain concerns sexual risk-taking, usually conceptualized as unsafe sex and promiscuity. However, psychopaths also may engage in sexual violence, including the use of coercive tactics in order to obtain sex. The present study was designed in two parts (counterbalanced) to further our understanding of the relation between psychopathic traits and sexual coercion. Part 1 will investigate the association between psychopathic traits, sexual risk, and use of both overt (e.g., using physical force, use of drugs or alcohol) and covert (e.g., massaging, sweet talking, guilt-tripping) sexual coercion strategies. Part 2 will examine whether psychopathic traits alter perceptions of sexual coercion. In particular, participants will be presented with a vignette that varies according to the level of sexual coercion (low/high), type of sexual coercion (verbal/physical/both), and whether sexual consent was granted following the use of these strategies or not. Following the vignette, participants will be asked to complete a judgment questionnaire concerning perceptions of consent, level of violence/coercion, criminal culpability, guilt, and sentencing severity. We predict that psychopathic traits will be associated with greater endorsement of use of sexual coercion, and a response pattern that indicates minimization of violence and blame for the vignettes. Faculty Mentor: Kristine Peace Department: Psychology (Honours)
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.057 | 0.020 |
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".