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Record W2752277348 · doi:10.1080/13501763.2017.1363807

Introduction: the European Semester as a new architecture of EU socioeconomic governance in theory and practice

2017· article· en· W2752277348 on OpenAlexafffund
Amy Verdun, Jonathan Zeitlin

Bibliographic record

VenueJournal of European Public Policy · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversity of Victoria
FundersSocial Sciences and Humanities Research Council of CanadaVrije Universiteit AmsterdamUniversiteit van AmsterdamEuropean Commission
KeywordsSocioeconomic statusMulti-level governanceCorporate governanceArchitectureEuropean unionEconomic governancePolitical sciencePublic administrationSociologyRegional scienceEconomicsManagementInternational tradeGeographyDemography

Abstract

fetched live from OpenAlex

The ‘European Semester’, a new framework for policy co-ordination across European Union (EU) member states, represents a major step in EU governance. Created in 2010 in the wake of the financial and sovereign debt crises and revamped in 2015, it was intended to provide a new socioeconomic governance architecture to co-ordinate national policies without transferring full sovereignty to the EU level. This introduction offers a brief overview and assessment of the European Semester, examining its implications along three critical axes, running respectively between the economic and the social, the supranational and the intergovernmental, and the technocratic and democratic poles of EU governance. We introduce and briefly summarize the seven other contributions that make up this collection. Our conclusions are that the European Semester challenges established theoretical understandings of EU governance, as it is a prime example of the complexity that supersedes simple polar oppositions.

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)0.004
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Editorial · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.005
Scholarly communication0.0080.006
Open science0.0010.005
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0130.001

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 head0.021
GPT teacher head0.328
Teacher spread0.307 · 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 designNot applicable
Domainnot available
GenreEditorial

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

Citations166
Published2017
Admission routes2
Has abstractyes

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