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Supranationalization through Socialization in the Council of the European Union

2007· article· en· W22849826 on OpenAlexfundno aff
Jakob Lempp, Janko. Altenschmidt

Bibliographic record

VenueBritish Journal of Pharmacology · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsnot available
FundersCanadian Institutes of Health Research
KeywordsResizingPolitical sciencePresidencySocializationReciprocity (cultural anthropology)European unionPublic administrationImpartialityMechanism (biology)CommissionCouncil of MinistersParliamentLawPublic relationsSociologyBusinessInternational tradeSocial sciencePolitics

Abstract

fetched live from OpenAlex

It is an academic truism that enlargement affected the functioning of the European Union and its institutions, and that effects of enlargement are especially noticeable in the Council and its sub-structures. Many researchers expected procedures in the Council to become more intergovernmental and decision-making to become more complicated. However, enlargement also contributed to institutional change in the Committee of Permanent Representatives in quite another—unexpected—way: it strengthened the influence of supranational and “quasi-supranational” actors within the Council, such as the Commission, the Presidency and the General Secretariat, and it made decision-making considerably easier in cases where profound national interests of the newcomers were not directly concerned. Four institutional mechanisms can be identified that contributed to this unexpected institutional evolution: The mechanism of socialization, the mechanism of specific and unspecific reciprocity, the mechanism of lack of interest and the mechanism of presidential impartiality. These mechanisms helped to overcome the cleavage between old and new as well as to uphold the strong and often cited esprit de corps within the Council and its preparatory bodies. The paper analyzes these processes and tries to answer the question: how did these mechanisms contribute to a kind of supranationalization of the Council and its substructures after the last rounds of enlargement? The analysis is based on 51 semi-structured, intensive interviews with experts from the Council General Secretariat and from member states’ Permanent Representations.

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)0.010
metaresearch head score (Gemma)0.010
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 score0.026
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.009
Scholarly communication0.0060.003
Open science0.0010.009
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0070.000

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 head0.051
GPT teacher head0.344
Teacher spread0.293 · 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

Citations4
Published2007
Admission routes1
Has abstractyes

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