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Record W2272731683 · doi:10.30950/jcer.v12i1.698

Fifty Years of Public (Dis)Satisfaction with European Governance: Preferences, Europeanization and Support for the EU

2015· article· en· W2272731683 on OpenAlexfundno aff
Maurits van der Veen

Bibliographic record

VenueJournal of Contemporary European Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
FundersYork University
KeywordsEuropean unionCorporate governanceMulti-level governancePoliticsPreferencePolitical scienceConsistency (knowledge bases)European integrationScope (computer science)Public opinionPublicsPolitical economySociologyBusinessEconomicsInternational tradeLaw

Abstract

fetched live from OpenAlex

Since its beginnings in the 1950s, the policymaking scope and authority of the European Union have dramatically expanded across a wide range of issue areas. Yet much remains unknown about the interaction between public preferences for EU-level governance, changes in such governance and overall support for European integration. This article analyses surveys ranging from 1962 to 2010 to show that while support for integration in different policy areas has fluctuated over time, it has been surprisingly stable overall; moreover, the relative preference ordering across issue areas has been even more consistent. In addition, this consistency is not affected by changes in Europeanization, nor do such changes appear to be driven by the relative strength of preferences. Finally, issue-specific support for EU-level governance has an impact on overall EU support that becomes stronger as Europeanization in that issue area increases, an effect that increases further with greater political knowledge. These findings call into question understandings of rising Euroscepticism as a reaction to Europeanization taking place primarily in areas where publics oppose it. In addition, they indicate that public awareness of European integration is far greater than political knowledge tests appear to indicate.

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)0.017
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.605

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0170.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.238
GPT teacher head0.386
Teacher spread0.148 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2015
Admission routes1
Has abstractyes

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