Bibliographic record
Abstract
COMMON LANGUAGE FOR INTIMATE PARTNER VIOLENCE RISK APPRAISAL: AN EVIDENCE-BASED POLICING APPROACH The CELIA IPV Project is a police/researcher partnership between the Ontario Provincial Police, Edmonton Police Service and Saint John Police Force and researchers at the Waypoint Centre for Mental Health Care, MacEwan University, University of New Brunswick and the University of Toronto. CELIA stands for “Common Language for Intimate Partner Violence Risk Appraisal” The CELIA IPV Project began as a collaboration among experts in the field of interpersonal violence risk and led to seeking and successfully securing Partnership Development Grant funding through the Social Sciences and Humanities Research Council. The project was started by a team of researchers consisting of Dr. N. Zoe Hilton (Principal Investigator), co-investigators Dr. Mary Ann Campbell, Dr. Angela Eke, Dr. Sandy Jung, and Dr. Soyeon Kim, and collaborators Elke Ham and Dr. Karl Hanson. We are partnering to study and share evidence-based approaches for assessing risk of IPV. Effective risk management requires a common language for interpreting risk over time and place. The development of standardized risk levels that provide a common language for linking risk scores to risk management have been developed for criminal reoffending in general, and for sexual reoffending, but have not yet been developed for IPV. Also concerning is the fact that existing tools may not reflect the growing concerns police face in cases of non-physical coercive control and child abuse concurrent with IPV. These gaps weaken efforts to promote public safety. This collaboration among researchers and police partners in three provinces will lead to the creation of a shared research database, laying the foundation for large-scale studies of evidence-based policing of IPV for the first time in Canada.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.627 | 0.504 |
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".