The risk of assessment: Understanding service providers' use of risk assessment for intimate partner violence and homicide prevention with Indigenous populations
Bibliographic record
Abstract
Risk assessments for intimate partner violence focus on the risk a victim may face of being revictimized and/or the likelihood that a perpetrator will reoffend. In many cases, these risk assessments involve an actuarial assessment of these risks, paying little attention to contextual and historical risk factors. With the over-representation of Indigenous populations in intimate partner violence victimization and perpetration, it is imperative that risk assessments consider the impact of colonization on Indigenous people’s increased vulnerability to intimate partner violence. Few researchers have critiqued the implications of clinical and actuarial risk assessments on Indigenous people. In an effort to address this issue, this thesis: (1) takes stock of current risk assessment strategies used by Canada’s service providers in the anti-violence sector; (2) identifies useful “promising practices” and barriers to effective risk assessment as identified by service providers; (3) discusses the ways in which these findings can be used to conceptualize an alternative approach to risk assessment; and (4) provides recommendations for the future of risk assessment based on the shortcomings identified in both the literature and interviews with service providers. This research uses a convenience sample of 30 telephone interviews with service providers, all of which were conducted by the author, which include 17 in Manitoba, seven in British Columbia, five in Alberta, and one in Nunavut. The sectors represented include, police, shelters, healthcare, victim services, probation. Findings indicate that many service providers use a patchwork approach to risk assessment, combining elements of structured and unstructured tools and practices to suit the individual and address the complex interplay of individual and systemic factors.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".