Patterns of Misogynistic Violence in Canada: A Latent Class Analysis of Perpetrator Ideology, Power, and Legal Response
Bibliographic record
Abstract
Objective(s): The study aimed to identify patterns among cases of misogynistic violence in Canadian legal cases and explore how sexist ideology, victim-perpetrator power dynamics, and violence type interact. We also examined perpetrator demographics and whether specific case features were related to legal decisions. Hypotheses: We hypothesized that perpetrators with more extreme sexist ideologies would engage in more severe forms of violence, that victim-perpetrator power imbalances would influence violence context and legal outcomes, and that distinct perpetrator profiles would emerge. Methods: We conducted a secondary data analysis of 179 Canadian case files (1997-2023) that involved male perpetrators of misogynistic violence. Cases were coded for perpetrator characteristics (i.e., demographic information, including sexist ideology), victim characteristics (i.e., number of victims, demographic information), and case characteristics (i.e., setting, presence of witnesses). We analyzed violence prevalence and conducted a latent class analysis. Results: Verbal and emotional abuse were the most prevalent. The latent class analysis revealed four perpetrator classes: “Isolated Sexual Assailants,” “Virtual Sexist Harassers,” “Detached Verbal Abusers,” and “Complex Aggressors”. “Isolated Sexual Assailants” showed high probabilities of sexual assault in personal settings with close relationships. “Virtual Sexist Harassers” often engaged in online harassment and exhibited significantly more incel-like views and non-sexist ideologies (e.g., racism). “Detached Verbal Abusers” exhibited workplace verbal harassment and more favorable, but not statistically significant, legal outcomes. “Complex Aggressors” exhibited high probabilities of physical violence and verbal harassment, and significantly more incel-like views. Conclusions: Findings suggest the heterogeneity of misogynistic violence and that perpetrator ideology and context may meaningfully impact behavior. The results have implications for understanding gender-based violence in Canada, developing tailored interventions for perpetrators, and informing legal policy.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".