Concerns with police practice in investigations into the deaths of Indigenous people in Canada
Bibliographic record
Abstract
Inadequate death investigations for Missing and Murdered Indigenous People (MMIP) in Canada are a growing problem. Despite several government initiatives and specialised police task forces, Indigenous families continue to report that investigators fail to conduct complete investigations into their loved ones’ deaths. At the centre of many of these concerns is the role that police play, or do not play, in fulfilling standard investigative procedures. This paper presents a thematic analysis of concerns made by Indigenous people about MMIP investigations in Canada, including three in-depth case studies from Prince Rupert, B.C. and 53 family testimony transcripts from the National Inquiry into Missing and Murdered Women and Girls. Three themes on primary investigative concerns in Indigenous cases emerged including (1) police discrepancies and biases at the time of report intake, (2) failures to collect evidence and information during investigations, and (3) lack of officer communication and support for bereaved families. The discussion throughout identifies the role of the police within these investigative concerns and incorporates cautions from academic literature. The study includes both professional and community recommendations for policing institutions to improve future investigations into the deaths of Indigenous people and support the well-being of the bereaved.
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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 | 0.022 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.034 | 0.018 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.010 |
| 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".