Increasing Support for Victims of Sexual Assault through the Adoption of a Victim-Centered Approach to Police Investigations
Bibliographic record
Abstract
Police services across North America have been criticized for their lack of support for victims throughout the investigative process, especially when investigating crimes involving sexual violence. The Problem of Practice addressed in this Organizational Improvement Plan (OIP) focuses on the lack of victim support throughout sexual assault investigations in a large police service in Canada. Taking a victim-centered approach to sexual assault investigations pushes the police organization to consider victims’ needs and rights ahead of strictly gathering information throughout a sexual assault investigation.\nA gap analysis identified two main areas of focus for this (OIP): the need for enhanced training and resources for police officers and the need for increased oversight and accountability mechanisms of those investigating sexual assaults. A plan for implementing a tiered training approach along with easily accessible online materials and resources for police officers aims to increase their knowledge surrounding victim-centered, trauma-informed approaches to supporting victims of sexual assault. A reinvestment of several officers throughout the organization provides a cost-effective approach to ensuring the oversight and accountability measures are in place to conduct appropriate, victim-centered, sexual assault investigations.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| 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".