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Record W4390886173 · doi:10.1522/revueot.v32n3.1684

Comment concevoir les soins de santé dans une perspective de décroissance : l’exemple de la Clinique communautaire de Pointe-Saint-Charles, un collectif auto-organisé

2024· article· fr· W4390886173 on OpenAlexaffvenue
Julie Coquerel

Bibliographic record

VenueRevue Organisations & territoires · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Sciences and Governance
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

L’économie circulaire fait face à plusieurs enjeux : impossibilité du découplage absolu entre croissance et externalités négatives, effet rebond et faible prise en compte de la justice sociale (Calisto Friant et collab., 2020). La décroissance propose des solutions à ces défis : produire moins, partager plus, décider ensemble (Abraham, 2019). Cet article propose d’illustrer l’apport de la perspective décroissantiste aux débats concernant l’économie circulaire en prenant l’exemple du domaine de la santé. Le système de santé, axé sur le curatif et en croissance permanente, participe à la crise socioenvironnementale. La Clinique communautaire de Pointe-Saint-Charles se pose comme option de rechange au modèle dominant. Elle critique l’importance accordée à la santé curative par rapport à la santé préventive et défend que ce sont les conditions socioéconomiques de vie qui déterminent principalement l’état de santé des citoyens et citoyennes. La Clinique est une organisation qui se rapproche du commun, défini comme un collectif auto-organisé pour répondre aux besoins de ses membres.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.148
Threshold uncertainty score0.295

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0110.026
Scholarly communication0.0120.008
Open science0.0020.005
Research integrity0.0090.012
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 head0.019
GPT teacher head0.322
Teacher spread0.302 · 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.

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
Published2024
Admission routes2
Has abstractyes

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