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Record W3168975651 · doi:10.7202/1076379ar

Ce que l’on jardine : les « permis de végétaliser » de vingt municipalités françaises et le projet de la rue-jardin Kléber à Bordeaux

2021· article· fr· W3168975651 on OpenAlexvenueno aff
Aurélien Ramos

Bibliographic record

VenueIntermédialités Histoire et théorie des arts des lettres et des techniques · 2021
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArtPolitical science

Abstract

fetched live from OpenAlex

Cet article porte sur la transformation de la relation entre le jardin et le jardinage dans les politiques publiques urbaines visant à susciter le désir de jardiner chez les citadins. À partir du constat de l’instrumentalisation des pratiques de jardinage en ville et de la recrudescence de l’emploi du verbe « jardiner » dans des contextes n’ayant plus guère à voir avec le jardin, il s’agit de revenir à ce qui lie le geste à l’objet, la pratique au lieu. À partir de l’analyse de deux dispositifs publics utilisés pour mettre les citadins au jardinage — les « permis de végétaliser » de vingt municipalités françaises et le projet de la rue-jardin Kléber à Bordeaux — l’article cherche à voir si la convocation du verbe « jardiner » comme synonyme de faire agir les citadins dans le processus de production de l’espace urbain signifie que le jardin comme objet médiateur entre l’individu et le monde reste un horizon alternatif et souhaitable pour la ville.

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)0.002
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 score0.263
Threshold uncertainty score0.523

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.006
Scholarly communication0.0060.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.039
GPT teacher head0.322
Teacher spread0.283 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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