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Record W3119906412 · doi:10.15353/cjds.v8i4.523

Including Our Self In Struggle

2019· article· en· W3119906412 on OpenAlexvenueno aff
Peter Beresford

Bibliographic record

VenueCanadian Journal of Disability Studies · 2019
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsIdeologySociologyPerspective (graphical)AllianceTerminologyAction (physics)Public relationsMental healthRelation (database)Dominance (genetics)Equity (law)LawPsychologyPoliticsPolitical sciencePsychotherapist

Abstract

fetched live from OpenAlex

This article takes as its starting point the author’s personal perspective and long term personal experience as survivor and activist/researcher to explore the ways in which the alliance of neo-liberal ideology and the psychiatric system has resisted the impact of mental health service users’/survivors’ activism and instead sought to co-opt and subvert its language, ideas and initiatives. Drawing on the author’s perspective, it looks first at how this has happened in relation to the language of mental health, exploring specific terminology. Then it examines how this has happened in relation to key ideas associated with survivors’ collective action, including self-management, peer support and recovery. It show how ‘our’ ideas have been reconstituted to serve neo-liberal ideological goals. Thirdly it looks at how survivors’ innovations have been obstructed and taken over instead by the dominant bio-medical paradigm. Finally it traces the way in which survivor knowledge has similarly been obstructed and appropriated. The article ends with discussion of ‘two beacons of hope’; the emergence of Mad Studies and ‘Gap-mending’ which offer the possibility of challenging neo-liberal dominance and emphasises the need to support and safeguard these developing opportunities.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.355
GPT teacher head0.483
Teacher spread0.129 · 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 designObservational
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

Citations11
Published2019
Admission routes1
Has abstractyes

Explore more

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