Domestic Violence in Cochrane: What RCMP Data Reveals About Perpetration
Bibliographic record
Abstract
Developed through the Made-In-Alberta Rural and Small-Town Collaborative, this study – the first-of-its-kind in Canada – analyzes five years of RCMP data (2020–2024) and a closer look at data from 2024 to understand patterns and trends in domestic violence perpetration in Cochrane and surrounding areas. The findings show that reported incidents of domestic violence have increased by 59% over five years. Most male perpetrators charged (68%) had prior domestic violence encounters with police, and three-quarters (75%) had previous criminal charges, underscoring the need for early intervention and targeted prevention strategies. The analysis highlights how data can be used proactively to inform community-led prevention efforts — helping local organizations, police, and leaders identify where supports, education, and accountability structures are most needed to stop violence before it starts. The Made-In-Alberta Rural and Small-Town Collaborative — including Shift, YWCA Banff, Big Hill Haven in Cochrane, and Rowan House Society in High River — is working to examine the root causes of violence, identify risk factors for male perpetration, and co-create local, evidence-based solutions to prevent harm.
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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 | 0.005 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".