Medical-Legal Alliances: Encounters with Excited Delirium in Ontario Coronial Law
Bibliographic record
Abstract
This theoretical paper analyzes Ontario coroner inquest reports that reference excited delirium from 1996 to 2023. The author argues that coroner inquest reporting engaged medical experts in work to exonerate law enforcement of white supremacist violence. Excited delirium as a racializing assemblage illustrates how the coroner inquest functions as a medico-legal tool that pulls focus from, and in so doing is designed to maintain, the violent institution of policing. To that end the author describes the anti-racist abolitionist theoretical approach driving this paper’s analysis, to show the limitations of reliance on what is ultimately a reformist response to death-by-police. Through this lens the author explains the invention and development of excited delirium in medical scholarship. Then in a review of Ontario coroner inquest reporting, the author shows how the causes of death identified and the summaries of death presented come to constitute excited delirium, both by focusing on conditions located in the body-mind of the deceased, and by reframing – and ultimately displacing legal scrutiny away from – restraint use and other patterns of violence found in police encounters. Further, jury recommendations and coroner elaborations related to training and research align with a reformist ethos that enlists medical authorities in the work of keeping institutions of policing intact and beyond meaningful reproach despite the violence they continually enact.
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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 | 0.005 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".