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Record W4410839831 · doi:10.1177/14651165251340212

When the EU Council responds to public opinion: Negotiating European policy integration

2025· article· en· W4410839831 on OpenAlexfundno aff
Nikoleta Yordanova, Anastasia Ershova, Aleksandra Khokhlova, Saad Obaid Ul-Islam, Goran Glavašš

Bibliographic record

VenueEuropean Union Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
FundersEconomic and Social Research CouncilNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversiteit LeidenDeutsche ForschungsgemeinschaftQueen's University BelfastQueen's UniversityEuropean CommissionLeverhulme Trust
KeywordsPolitical sciencePublic opinionNegotiationEuropean unionPublic administrationEuropean integrationPolitical economyPoliticsLawSociologyInternational tradeBusiness

Abstract

fetched live from OpenAlex

While the Council of the European Union has long been deciding on EU policy insulated from public scrutiny, we argue that enhanced transparency and EU politicisation have strengthened the linkage between its positions and public opinion. We further expect the Council to be more responsive to public opinion in member states, in which citizens view EU policy action as salient and are relatively united in their stance on it. To assess these expectations, we used semi-supervised machine learning to estimate the Council's positions on the expansion of the EU policy authority in legislative acts during the post-Lisbon period (2009–2019) and the Eurobarometer to measure public support for EU action in 21 policies across member states. The results offer evidence of territorial responsiveness of the Council.

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)0.053
metaresearch head score (Gemma)0.127
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.280

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0530.127
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0040.008
Scholarly communication0.0160.008
Open science0.0010.008
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.071
GPT teacher head0.325
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; 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

Citations0
Published2025
Admission routes1
Has abstractyes

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