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Record W4409515782 · doi:10.26522/ssj.v19i1.4720

Medical-Legal Alliances: Encounters with Excited Delirium in Ontario Coronial Law

2025· article· en· W4409515782 on OpenAlexaffvenueabout
Jen Rinaldi

Bibliographic record

VenueStudies in Social Justice · 2025
Typearticle
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsLawDeliriumPolitical sciencePsychologyCriminologyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.864

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.050
GPT teacher head0.409
Teacher spread0.359 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

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