Profile and Programming Needs of Federal Offenders With Histories of Intimate Partner Violence
Bibliographic record
Abstract
This study presents data on male perpetrators of domestic violence (DV) in the Correctional Service of Canada (CSC) using two samples: (a) a snapshot of all male offenders in CSC who had been assessed for DV (n = 15,166) and (b) a cumulative sample of male offenders in CSC from 2002-2010 who had been assessed as moderate or high risk for further DV (n = 4,261) DV offenders were compared to a cohort sample of non-DV offenders (n = 4,261). Analyses were disaggregated for Aboriginal and non-Aboriginal offenders. Results indicated that 40% of the federal male population had a suspected history of DV and were therefore screened in for in-depth DV risk assessment. Of these, 45% were assessed as moderate or high risk for future DV. DV offenders had higher risk and criminogenic need ratings, more learning disabilities, more mental health problems, and more extensive criminal histories than those without DV histories. Aboriginal DV offenders had high levels of alcohol dependence, suggesting a need for substance abuse treatment as part of DV programming. Most federal offenders with DV histories would be described as belonging to the Antisocial/Generalized Aggressive typology and, therefore, adhering to the Risk-Need-Responsivity principles of the effective correctional literature, cognitive-behavioral treatment that focuses on teaching skills of self-management, and changing attitudes supporting relationship violence would be recommended.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".