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Record W2908056373 · doi:10.1080/24694452.2018.1473753

Well Enough to Work? Social Enterprise Employment and the Geographies of Mental Health Recovery

2019· article· en· W2908056373 on OpenAlexafffundabout
Joshua Evans, Robert Wilton

Bibliographic record

VenueAnnals of the American Association of Geographers · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsMcMaster UniversityUniversity of Alberta
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsSubjectivityMental healthMainstreamMeaning (existential)SociologyContext (archaeology)Work (physics)ProductivityPublic relationsEconomic growthPsychologyPolitical scienceEconomicsEpistemologyLaw

Abstract

fetched live from OpenAlex

This article examines the significance of paid work and workplaces for people living with mental ill health. Employment and workplaces have been largely absent in the mental health geography literature in part because of the persistent problems that people with mental ill health face in finding and retaining paid work; yet paid work and questions of productivity remain central to the very meaning of mental illness in capitalist society. To address this gap, we report on research involving social enterprises in Canada that reduce barriers to participation in paid work. Through the provision of accommodations and supports, these enterprise sites challenge the disabling division of labor characteristic of mainstream workplaces. In so doing, they provide a context in which people, understanding themselves as “well enough to work,” can enact new forms of economic subjectivity. The meaning of paid work in these alternative sites remains defined in relation to the norms of the capitalist economy, however. Thinking beyond these narrowly defined conceptions of wellness and productivity offers an important avenue for future mental health geographies.

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)0.002
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 score0.046
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.021
GPT teacher head0.367
Teacher spread0.346 · 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

Citations22
Published2019
Admission routes3
Has abstractyes

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