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Record W1832914005 · doi:10.4000/vertigo.16248

La représentation de la nature par les collectivités territoriales devant le juge judiciaire à la lumière de l’article L.142-4 du Code de l’environnement

2015· article· fr· W1832914005 on OpenAlexvenueno aff
Marthe Lucas

Bibliographic record

VenueVertigO · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Urban and Social Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Ayant été introduit avant l’aboutissement du contentieux de l’Erika, l’article L. 142-4 du Code de l’environnement a permis à certaines collectivités territoriales de se constituer partie civile et d’obtenir la réparation du préjudice écologique. Six ans après son introduction, notre article s’interroge sur les apports et les enjeux de cette disposition. Il convient de saluer l’opportunité de l’habilitation à agir des collectivités territoriales en ce qu’elle élargit le cercle des acteurs agissant en représentation de la nature et permet de contrebalancer l'inertie de certains. La complémentarité de l’action des collectivités territoriales, notamment vis-à-vis des associations agréées, va cependant dépendre du contenu donné par les juges au « préjudice causé au territoire sur lequel elles exercent leurs compétences ». D’un point de vue pratique, l’étude de la jurisprudence actuelle révèle les confusions des parties et du juge à qualifier ce que recouvre le préjudice subi par les collectivités. Enfin, l’article L. 142-4 du Code de l’environnement ne résout pas les difficultés tenant au risque de cumul de réparation du préjudice écologique et à la réalisation de mesures de réparation en nature sur un site dont les collectivités territoriales ne sont pas propriétaires.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0050.009
Scholarly communication0.0080.003
Open science0.0010.003
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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 head0.022
GPT teacher head0.284
Teacher spread0.262 · 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 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
Published2015
Admission routes1
Has abstractyes

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Same venueVertigO→Same topicFrench Urban and Social Studies→French-language works237,207→