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Record W3101462400 · doi:10.22215/etd/2020-14154

The Political Economy of Psychiatry - Power, Knowledge, and Subjectification in the State/Psychiatry Apparatus

2020· dissertation· en· W3101462400 on OpenAlexaff
Darrin Antler

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldArts and Humanities
TopicMental Health and Psychiatry
Canadian institutionsCarleton UniversityResearch Canada
Fundersnot available
KeywordsSubjectificationPsychiatryMental healthPower (physics)Privilege (computing)Mental illnessState (computer science)PoliticsHistory of psychiatryMilitary psychiatryPsychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Mental Health is an issue of great importance to states and governments worldwide.Mental illness is the number one cause of disabilities in Canada and globally.Psychiatry dominates mental health knowledge, discourses and policy, prioritizing a biomedical approach, producing questionable results.The concept of psychiatric disorders is criticized for lacking scientific reliability and validity, psychopharmaceuticals for inefficacy and danger, and coercive practices for violating human rights.Why then does psychiatry continue to dominate mental health policy, practices, discourses and understandings?Applying critical theory of political economy with influences from Marx, Foucault, and others, this thesis argues psychiatry's power and privilege persists through the existence of the state/psychiatry apparatus, consolidating knowledge and power in a process of psychiatric subjectification, creating psychiatric subjects -mentally ill persons.The theory is applied to Canada and Ontario, mapping and explaining the parameters and layers of the apparatus and the political economy of psychiatry.

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)0.009
metaresearch head score (Gemma)0.009
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.992
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.041
Scholarly communication0.0120.005
Open science0.0000.004
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0070.001

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 head0.018
GPT teacher head0.291
Teacher spread0.273 · 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 designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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