Bibliographic record
Abstract
The purpose of this study is to extend the rape myth literature to intimate partner violence (IPV) myths by evaluating the prevalence of IPV myth acceptance and clarifying whether gender and prior IPV victimization are associated with IPV myth acceptance. To this end, three research questions were explored: 1) What is the prevalence of IPV myth acceptance amongst a student population? 2) Does gender correlate with IPV myth acceptance? 3) Do victims and non-victims of IPV accept IPV myths differently? University of Alberta students were contacted via posters and classroom presentations to participate in a 15-minute online survey containing a demographic survey, the Marlow-Crown Social Desirability Scale (MC-C; Reynolds, 1982), the Domestic Violence Myth Acceptance scale (DVMAS; Peters, 2003), and three subscales from the Revised Conflict Tactics Scale (CTS2; Straus et al., 1996). Depending upon the criteria used to define acceptance, between 65% (neither agreeing nor disagreeing that “domestic violence rarely happens in my neighbourhood”) and 11% (strongly agreeing that “if a woman doesn’t like it she can leave”) of participants accepted at least one IPV myth. Consistent with expectations, men accepted IPV myths to a greater extent than women, and victims of IPV did not differ from non-victims in their acceptance of IPV myths.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".