To Whom Can We Turn?” Evaluating the Adequacy of Police and Coroner Investigations into Questionable Deaths
Bibliographic record
Abstract
Canada has witnessed thousands of Indigenous testimonies about the suspicious deaths and disappearances of their loved ones and the deficient or non-existent investigations thereafter. Despite growing attention to Indigenous deaths in inquiries and government apologies, there remains little information at ground level for families on how to challenge investigative practices and few cases that have done so successfully. Our research began when we were invited to evaluate the investigations into the suspicious deaths of three Indigenous youth in Canada. We did so by comparing police and coroner behaviour in those cases to standard practices required by provincial, federal, and international guidelines for police and coroners. Results revealed numerous instances of inadequacy where investigators either did not perform required procedure(s) or did not complete tasks to internationally recognized standards; police and the coroner performed half or fewer of “required” procedures in each of the three cases. An important product of our evaluation is a checklist of standard investigative procedures that other families and communities can use to assess other investigations into questionable deaths that occur in their communities and press for accountability.
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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 | 0.188 | 0.466 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".