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Record W3035950214 · doi:10.22215/cjers.v13i2.2563

Economic and Social Balance of 15 Years of Eastern Enlargement

2020· article· en· W3035950214 on OpenAlexvenueno aff
Béla Galgóczi

Bibliographic record

VenueThe Canadian Journal of European and Russian Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsResizingConvergence (economics)Member statesBalance (ability)ProductivityEconomicsCore (optical fiber)Economic systemEconomic geographyDevelopment economicsPolitical scienceEuropean unionInternational tradeEconomic growthEngineeringPsychology

Abstract

fetched live from OpenAlex

The Eastern EU enlargement (2004, 2007, 2013) is still one of the success stories of the EU (and unprecedented in the world), but at the same time it is controversial and is perceived as controversial. One of the core problems has been its unbalanced character: the whole process had a clear `Single Market` focus and the values of a `Social Europe` were of secondary importance. Based on a neofunctionalist approach the paper discusses the integration of the new member states from the point of view of economic and income convergence. Along with a literature review, data on wages, productivity and output will be analysed to demonstrate that upward convergence of the poorer new member states towards the EU average had been stalled in wake of the 2009 crisis. The resulting cleavages put the core hypothesis of the neofunctionalist approach - that EU integration has a `direction` in terms of an upwards convergence - into question.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.002
Scholarly communication0.0040.003
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.060
GPT teacher head0.287
Teacher spread0.226 · 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 designObservational
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

Citations2
Published2020
Admission routes1
Has abstractyes

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