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Record W4239472876 · doi:10.22215/rera.v8i1.225

Europeanization in EU External Relations after the Eastward Enlargement: Complications and Bypasses to Greater Engagement with the Eastern ENP Countries

2013· article· en· W4239472876 on OpenAlexaffvenue
Ivan F. Dumka

Bibliographic record

VenueReview of European and Russian Affairs · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsResizingEliteNormativePolitical scienceInternational relationsPolitical economySocializationEuropean unionPoliticsEconomic systemInternational tradeSociologyLawBusinessEconomics

Abstract

fetched live from OpenAlex

Emphasizing Poland and its relations with Ukraine, this paper applies a Europeanization framework to examine the uploading of external relations policies by EU members. It argues that as enlargement has shortened the list of countries to which the EU has made membership commitments, normative entrapment will not be at work in its external relations, nor address the more fractious nature of EU decision-making brought on by a larger and more diverse membership. This results in strategic behaviour by EU members and more laboured decision making, which can be expected, in general, to complicate the EU's external relations. Simply put, the coalition building that is so central to EU policymaking is more difficult following the eastward enlargement. However, because the new members vote, collaborate, and build coalitions in favour of closer ties to these eastern neighbours, complications from enlargement should be far less pronounced in the eastern policy than with other ENP countries. This comes despite striking shortcomings by Poland in the administrative capacity and elite socialization that normally characterize those member states who often succeed at projecting their preferences onto EU policy. All of this means that one can expect an eastward shift in the focus of the EU's external relations, and a deepening of its differentiated approach to external relations.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.954
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.016
GPT teacher head0.260
Teacher spread0.244 · 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 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

Citations0
Published2013
Admission routes2
Has abstractyes

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