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Record W2619451054 · doi:10.4000/ere.332

Construction d'un partenariat de connaissances sur les questions de justice environnementale : Exemple du projet européen EJOLT

2016· article· fr· W2619451054 on OpenAlexvenueno aff
Jean-Marc Douguet, Vahinala Raharinirina, Martin O’Connor, Philippe Roman

Bibliographic record

VenueÉducation relative à l environnement · 2016
Typearticle
Languagefr
FieldSocial Sciences
TopicEnvironmental law and policy
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

Cet article traite du futur de la recherche associative comme la construction d’un partenariat de connaissances appliqué, dans le cas présent, au domaine de la justice environnementale. Le développement d'un nouveau web, un web « herméneutique », permet d'envisager de quelle manière il est possible de développer des interfaces science-société reposant sur un modèle politique de dialogue autour de la connaissance. Le portail de connaissances ePLANETe sur l'éducation environnementale, l'économie écologique et le développement soutenable envisage une telle évolution. Trois outils sont présentés dans cet article, afin de mobiliser les acteurs et leurs connaissances pour construire une représentation collective des conflits socio-environnementaux et pour délibérer autour des choix sociaux à effectuer pour aller vers des modes de gouvernance environnementaux plus justes.The purpose of this article is to address the future of associative research aiming at building a partnership of knowledge, related in this article to environmental justice issues. The development of a new web, a "hermeneutic" web, makes us consider how possible it is to develop a science-society interface based on a political model of dialogue on knowledge. The ePLANETe knowledge portal on environmental education, ecological economics and sustainability anticipates such developments. Three tools are presented in this article, in order to mobilize actors and knowledge to build a collective representation of socio-environmental conflicts, to deliberate around social choice, and to make more accurate environmental governance.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.003
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.292
Teacher spread0.254 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2016
Admission routes1
Has abstractyes

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