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Record W2998928965 · doi:10.7202/1066626ar

La résistible émergence d’une gouvernance participative à Delhi

2019· article· fr· W2998928965 on OpenAlexvenueno aff
Stéphanie Tawa Lama‐Rewal

Bibliographic record

VenueRevue Gouvernance · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
FundersAgence Nationale de la Recherche
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Ville majeure mais État faible, Delhi est, depuis 2000, un laboratoire de la démocratie participative, notamment parce que c’est là qu’a émergé en 2013 l’Aam Aadmi Party (AAP, Parti de l’homme ordinaire), un parti qui place la participation au coeur de son projet politique. Cet article analyse ce qui fait à la fois la nouveauté et la faiblesse de la gouvernance participative proposée par l’AAP depuis sa large victoire électorale de 2015, en situant cette gouvernance par rapport aux pratiques antérieures. En comparant la destinée de trois dispositifs successivement mis en oeuvre au cours des deux dernières décennies dans la capitale indienne – le programme Bhagidari, les mohalla sabhas (assemblées de quartier) et les SMC mahasabhas (assemblées scolaires) – il propose une « analyse contextualisée de la participation » (Mazeaud, Boas, et Berthomé, 2012) et contribue ainsi au dialogue nécessaire entre études des dispositifs participatifs et études de la gouvernance (Melo et Baiocchi, 2006). Plus spécifiquement, en interrogeant à la fois le sens et la réception des principes d’action publique regroupés sous le nom de gouvernance participative, cet article met en évidence le rôle de deux acteurs de la gouvernance urbaine – la bureaucratie et la société civile organisée – dans la longévité du programme Bhagidari et la brièveté des assemblées de quartier à Delhi.

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)0.002
metaresearch head score (Gemma)0.004
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.125

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0080.009
Scholarly communication0.0080.002
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.001

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 head0.036
GPT teacher head0.273
Teacher spread0.237 · 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
Published2019
Admission routes1
Has abstractyes

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