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Record W4283213768 · doi:10.1111/jcms.13365

One Big Conversation: The EU's Climate Diplomacy across the International Regime Complex on the Paris Agreement Negotiations

2022· article· en· W4283213768 on OpenAlex
Joseph Earsom, Tom Delreux

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJCMS Journal of Common Market Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
FundersFonds De La Recherche Scientifique - FNRS
KeywordsNegotiationDiplomacyConversationPolitical sciencePoliticsMomentum (technical analysis)International relationsClimate changeAgreementPolitical economySociologyBusinessLaw

Abstract

fetched live from OpenAlex

Abstract The EU participates in many international fora related to climate change (for example UNFCCC, G20, Montreal Protocol), which collectively constitute the international regime complex on climate change (IRCCC). Using the case study of negotiations on the Paris Agreement, this paper addresses the question How and why did the EU use the different fora of the IRCCC to achieve its objectives in the Paris Agreement negotiations? It finds that the EU used the IRCCC in four main ways: employing typical multilateral negotiating activities, overcoming specific issues of the Paris Agreement negotiations, creating political momentum, and ensuring cross‐fora coordination. These uses correspond with the level of political authority of participants and the level of climate‐specialization in a given forum.

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.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.903
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.121
GPT teacher head0.379
Teacher spread0.259 · 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