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Record W4392748180 · doi:10.7202/1109702ar

Les jeunes dans les négociations climatiques internationales entre marginalisation et contestation

2022· article· fr· W4392748180 on OpenAlexvenueno aff
Maxime Gaborit, Amandine Orsini, Yi hyun Kang

Bibliographic record

VenueÉtudes internationales · 2022
Typearticle
Languagefr
FieldSocial Sciences
TopicSocial Media and Politics
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationPolitical scienceSociologyLaw

Abstract

fetched live from OpenAlex

À partir d’une enquête ethnographique autour de la 26e Conférence de la Convention-cadre des Nations Unies sur les changements climatiques, cet article analyse les modalités de participation des jeunes dans cette arène. Nous montrons comment ces acteurs se caractérisent par des ancrages sociodémographiques particuliers et des modes d’engagement divers. Au-delà de la prétention à représenter la jeunesse mondiale, leur identité de « jeune » se construit progressivement par la commune affirmation d’une forte ambition sur les enjeux climatiques, mais aussi par la reconnaissance de leur relative inexpérience du fonctionnement des négociations, qui renforce leur marginalisation. Si des échanges existent entre jeunes activistes et jeunes institutionnalisés, leurs positions précaires produisent des tensions. Pour se faire entendre, la frange la plus institutionnalisée au sein des négociations a massivement investi les enjeux d’éducation et d’inclusion, aux dépens d’une position audible sur d’autres enjeux. La frange la plus activiste, déçue du processus, s’est quant à elle montrée prompte à une critique plus radicale du système économique et des modalités des discussions. Malgré les tensions, les jeunes, acteurs atypiques des négociations internationales, s’unissent dans un investissement conséquent des arènes internationales.

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)0.006
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 score0.030
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0120.019
Scholarly communication0.0090.005
Open science0.0010.008
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.366
Teacher spread0.299 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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Same venueÉtudes internationalesSame topicSocial Media and PoliticsFrench-language works237,207