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Record W4399182604 · doi:10.1017/ipo.2023.30

Explaining the politicization of EU trade agreement negotiations over the past 30 years

2024· article· en· W4399182604 on OpenAlexaboutno aff
Luca Cabras

Bibliographic record

VenueItalian Political Science Review/Rivista Italiana di Scienza Politica · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicPolitical Influence and Corporate Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsTransatlantic Trade and Investment PartnershipFree tradeInternational tradeTreatyEuropean unionNegotiationInternational economicsLiberalizationGeneral partnershipTrade agreementCommercial policyTrade barrierPolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

Abstract Although the Transatlantic Trade and Investment Partnership with the USA and the Comprehensive Economic and Trade Agreement with Canada have elicited considerable domestic contestation in Europe, several other agreements have been negotiated into public and media indifference. What explains this difference? In this article, I put forward a number of arguments on the structural causes of the politicization of European Union (EU) trade policy over the past 30 years and test them against a newly collected dataset covering 19 preferential trade agreements. The qualitative comparative analysis suggests that the politicization of EU trade negotiations is determined by the co-occurrence of several, well-defined conditions. More specifically, it tells us that: (1) the Lisbon Treaty's reform of EU trade policymaking is the main driver of politicization, (2) the level of public support for the EU is of particular relevance when it comes to ‘deep and comprehensive’ agreements that touch on sensitive domestic issues, and that (3) high adjustment costs expected from trade liberalization can lead to the politicization of trade negotiations.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.729
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.004
Science and technology studies0.0010.005
Scholarly communication0.0020.003
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.035
GPT teacher head0.303
Teacher spread0.268 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations3
Published2024
Admission routes1
Has abstractyes

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