Strategies and recommendations for managing high-risk intimate partner violence (IPV) offenders in British Columbia
Bibliographic record
Abstract
High-risk offenders of intimate partner violence (IPV) have the highest probability of engaging in severe violence or causing death (Stewart & Power, 2014). Between 2005 to 2015, 100 deaths occurred in British Columbia as a result of IPV (BC Coroners Service Death Review Panel, 2016). As these high-risk IPV offenders underlying causes of the violence are complex, it is essential to understand strategies that can be used to decrease their likelihood of serious injury or homicide. Various initiatives across North America have been established to classify, monitor and rehabilitate these dangerous individuals; similarly, British Columbia has emphasized the need to focus on high-risk IPV offenders. Although considerable progress has been made, a further emphasis on education, evaluation, and evidence-based practices would be valuable. In addition, British Columbia would benefit from implementing preventative measures such as public awareness campaigns and youth education programs focused on healthy relationships. Lastly, there is a strong need for a national policy on DV to enhance prevention, response, and management of high-risk IPV offenders.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".