Severity and Predictors of Physical Intimate Partner Violence against Male Victims in Canada
Bibliographic record
Abstract
Recent debates surrounding intimate partner violence (IPV) have focused on its gender symmetry and gender-oriented nature. These debates center on findings from various data sources, like victimization or self-reported surveys and police-based reports. Data by Statistics Canada, from 1999 to 2014, has shown that the prevalence of IPV is similar for male and female victims, except for sexual assaults. However, there has been a paucity of studies on the severity and risk factors of IPV against men by female partners. Thus, this paper examines the severity of and risk factors for physical IPV against heterosexual men in Canada using the General Social Survey (Victimization) data of 2014. This study revealed that there is a symmetry in the experiences of physical violence between male and female victims. This study also revealed that male victims experience more severe violence than female victims. Using binary logistic regression analysis, years of dwelling together, the victim’s age, childhood victimization, and marijuana use were found to predict physical IPV against heterosexual men. This paper concludes with suggestions about how these predicting factors can be used to identify male victims and the need for a more inclusive approach toward addressing IPV, which should include male victims.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".