Book Note: Dying From Improvement: Inquests And Inquiries Into Indigenous Deaths In Custody, by Sherene H. Razack
Bibliographic record
Abstract
INDIGENOUS DEATHS IN CUSTODY have long told a story of alcoholism and mental illness, suggesting a fundamental incapacity of Canada’s Indigenous populations to survive and prosper in modern society. In Dying from Improvement Sherene H. Razack tells a different story, one of colonial aggression which constructs the state as a body that secures its own legitimacy through encounters with Indigenous bodies. Running parallel to this examination is a question for all Canadians: In the face of police aggression and the profound indifference that underlies so many Indigenous deaths in custody, “why do we fail to care?”2 By examining inquests and inquiries into Indigenous deaths in custody in British Columbia and Saskatchewan, the author shines a light on the oft-subverted discussion of the racial animus that colours police interaction with Canada’s Indigenous peoples. Razack is primarily concerned with challenging the Canadian public discourse that defines Indigenous deaths in custody—the story of a group of people unfit for modern life who exist somewhere “between life and death,” as prisoners of their own dysfunction and dependency.3The book is divided into six chapters. Each chapter examines the death of a particular person or group, and state response to the associated tragedy, running alongside an exploration of what the author believes lies beneath the surface.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.009 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".