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Record W4381888607 · doi:10.1080/13501763.2023.2217849

Missed opportunities: the impact of internal compartmentalisation on EU diplomacy across the international regime complex on climate change

2023· article· en· W4381888607 on OpenAlexaboutno aff
Tom Delreux, Joseph Earsom

Bibliographic record

VenueJournal of European Public Policy · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicClimate Change Policy and Economics
Canadian institutionsnot available
FundersFonds De La Recherche Scientifique - FNRS
KeywordsDiplomacyNegotiationClimate changePolitical scienceCorporate governanceInternational regimeClimate governanceMulti-level governanceInternational tradeInternational lawInternational relationsPolitical economyEuropean unionEconomicsLawPoliticsEcology

Abstract

fetched live from OpenAlex

International climate governance no longer takes place only in the UNFCCC but is spread across numerous fora that collectively form the international regime complex on climate change. For climate leaders like the EU, the regime complex creates opportunities for strategic activity in its diplomacy across the different fora. This article examines how internal compartmentalisation affected the EU’s diplomacy across the international regime complex on climate change in the negotiation of four climate agreements: the Paris Agreement (UNFCCC), CORSIA (ICAO), the Kigali Amendment (Montreal Protocol), and the Initial Strategy (IMO). It finds that internal compartmentalisation indeed hinders the EU’s pursuit of a comprehensive climate diplomacy, making the regime complex a missed opportunity for the EU. Various combinations of a lack of communication channels, different priorities and policy framings, and a lack of resources and expertise contributed to situations where the EU was limited in using the regime complex.

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)0.021
metaresearch head score (Gemma)0.055
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.055
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0080.014
Scholarly communication0.0190.015
Open science0.0010.021
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0100.001

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 head0.479
GPT teacher head0.386
Teacher spread0.093 · 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 designObservational
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

Citations5
Published2023
Admission routes1
Has abstractyes

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Same venueJournal of European Public PolicySame topicClimate Change Policy and EconomicsFrench-language works237,207