Exploring Typologies of Domestic Violence Perpetrators: Insights into Male Patterns and Behaviours
Bibliographic record
Abstract
Research into domestic violence has typically focused on the victims, who are usually female. However, shifting the focus to the male perpetrators creates opportunities for earlier intervention to stop the violence. By recognizing the early warning signs, police, community-based supports and governments can target interventions to prevent domestic violence before it escalates or even occurs. This policy brief examines the 10-year history of Calgary Police Service interactions with 934 Calgary men, aged 18 and above, who were eventually charged in domestic violence incidents in 2019. Based on their criminal and domestic encounters with police, the perpetrators fell into four typologies. Of the four groups, one had no history with police and the second had a criminal history but no non-criminal domestic encounters before the 2019 charge. The third group had a history of non-criminal domestic encounters, but no criminal history with police, while the fourth group had a record of both criminal charges and non-criminal domestic encounters with police. Only 27 per cent of the men in this study had no previous interactions with police. These trajectories and typologies reveal discernible increases in criminal activity and domestic encounters with police culminating in domestic violence charges. This information can help to focus legislation, policies and practices which can lead to preventing domestic violence, thus improving on the current model in which police and community organizations often respond to domestic violence only after the fact. Increased police interactions prior to a criminal conviction involving domestic violence mean there is a point at which early intervention may prevent a criminal incident of domestic violence from happening. Interventions can include providing access to counselling and supports while making online resources accessible to men at risk of becoming perpetrators and who are struggling with their behaviour in their intimate relationships. Other prevention efforts could include school-based programs and targeting male-dominated workplaces with domestic violence prevention efforts in order to avert potential first offences. The approach to domestic violence must shift. The victims’ responsibility to keep themselves safe needs to be augmented by a focus on stopping the individuals who perpetuate harm. Our ongoing research agenda is investigating the extent to which police, government and policy-makers may be able to use information about the behaviours and trajectories of offenders to intervene proactively and so prevent incidents of domestic violence from happening.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".