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Record W4361219376 · doi:10.3917/enic.hs12.0015

Justice environnementale et ressentiment vert : l’exemple d’arrondissements montréalais

2023· article· fr· W4361219376 on OpenAlexaffabout
Caroline Patsias

Bibliographic record

VenueLes Enjeux de l information et de la communication · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicEnvironmental Justice and Health Disparities
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPolitical scienceHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Cet article examine l’opposition des citoyens des classes moyennes et paupérisées aux politiques vertes dans deux arrondissements de Montréal. Via l’étude de conseils d’arrondissement, cette étude souligne la façon dont la justice environnementale nourrit un ressentiment vert chez certains citoyens. Les citoyens observés ne sont pas idéologiquement contre les politiques environnementales. Ils se sentent plutôt emportés par une « révolution verte » dont ils supporteront la majeure partie des coûts sans en retirer de bénéfices à court terme. Leur conception de la justice environnementale croise une critique procédurale de la démocratie concernant la participation et la transparence et une critique substantielle des inégalités dans la répartition des coûts des politiques environnementales. Ces citoyens mettent également en avant un attachement à leur quartier qui lie l’environnement à une conception du bien commun.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.639
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.038
GPT teacher head0.368
Teacher spread0.330 · 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.

Study designNot applicable
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
Published2023
Admission routes2
Has abstractyes

Explore more

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