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Record W2400848542 · doi:10.1177/1362480616646623

Spinning the revolving door: The governance of non-compliant psychiatric subjects on community treatment orders

2016· article· en· W2400848542 on OpenAlexaffabout
Amy Klassen

Bibliographic record

VenueTheoretical Criminology · 2016
Typearticle
Languageen
FieldPsychology
TopicHealthcare Decision-Making and Restraints
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRationalityRevolving doorCompliance (psychology)Corporate governancePsychiatrySubject (documents)PsychologyCriminologyComputer securityLawMedical emergencyMedicineSocial psychologyBusinessPolitical scienceComputer science

Abstract

fetched live from OpenAlex

This article examines the enactment of community treatment orders (CTOs) in Alberta, Canada to illustrate how civil law is used to constitute and govern psychiatric patients in the community. I argue that the logic of CTOs constitutes the psychiatric patient as a fractured subject who is simultaneously capable/incapable of making medical decisions and at risk/risky. These paradoxical characterizations highlight how depictions of rationality and choice are contingent on consenting to a pharmacological regime designed to normalize these patients. This construction functions to eliminate opportunities for rationally informed types of non-compliance and promotes hospitalization as the only way to manage harmful, risky and non-conforming individuals. I contend that CTOs are a flawed instrument of regulation that cannot manage ‘legally’ capable but non-compliant individuals.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.973
Threshold uncertainty score0.666

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.024
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0270.061
Scholarly communication0.0140.003
Open science0.0030.008
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0050.000

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 head0.083
GPT teacher head0.376
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations9
Published2016
Admission routes2
Has abstractyes

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