Taking Seriousness Seriously: Revisiting Gender Symmetry and Mutual Violence in Intimate Partner Violence through Role Types in Intimate Partner Violence Events Reported to the Police
Bibliographic record
Abstract
The study examines gender symmetry and asymmetry in intimate partner violence (IPV) reported to the police. A large database of police-recorded IPV events in British Columbia is analyzed over a four-year period (2009–2012) to examine the nature of gender symmetry, asymmetry, and mutual violence in the context of roles (as victims and perpetrators) in IPV. In accordance with past research, the findings show a pattern of gender asymmetry for IPV offences for women generally and across repeat IPV police contacts. Across repeat contacts with the police, the results indicate that the probability of being victimized repeatedly is much greater for females, although there is a significant amount of symmetrical violence, gauged by similar levels of victim and offender role counts, and mutual violence in the histories of females. In addition, as the number of associated events increases, a greater proportion of females have perpetrator role associations in their histories. The results indicate that in the context of mutual violence, females are likely to have a small number (i.e., one) of perpetrator roles within a history of victimization.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".