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Record W4392311828 · doi:10.4000/ebisu.9037

Problèmes environnementaux et pouvoir judiciaire au Japon

2023· article· fr· W4392311828 on OpenAlexaff
Noriko Okubo

Bibliographic record

VenueEbisu · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsGDG Environnement
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

Historiquement, le Japon reste l’un des premiers pays à avoir admis la spécificité des questions environnementales et la nécessité de leur judiciarisation : en pratique, le recours au juge s’est avéré particulièrement efficace pour la protection des victimes de pollution. Que le Japon, à rebours des tendances internationales, refuse de reconnaître les droits environnementaux et n’admette pas davantage les litiges d’intérêt public dans le domaine environnemental, soulève cependant plusieurs difficultés. À partir d’une mise en perspective critique du développement jurisprudentiel des droits de la personnalité, cet article interroge la portée des décisions de justice dans les domaines de la protection environnementale et du changement climatique.

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)0.006
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: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0130.017
Scholarly communication0.0100.004
Open science0.0010.005
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.042
GPT teacher head0.356
Teacher spread0.314 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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