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Record W2947194029 · doi:10.30950/jcer.v15i2.999

Measuring Economic Reform Recommendations under the European Semester: ‘One Size Fits All’ or Tailoring to Member States?

2019· article· en· W2947194029 on OpenAlexafffund
Valerie J. D’Erman, Jörg Haas, Daniel F. Schulz, Amy Verdun

Bibliographic record

VenueJournal of Contemporary European Research · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicEuropean Union Policy and Governance
Canadian institutionsUniversity of Victoria
FundersErasmus+Social Sciences and Humanities Research Council of CanadaEuropean Commission
KeywordsMember statesEuropean unionEconomic and monetary unionEconomicsWelfareEconomic welfarePublic economicsBusinessEconomic policyMarket economy

Abstract

fetched live from OpenAlex

In 2010 the European Semester was created to better coordinate fiscal and economic policies within Europe’s Economic and Monetary Union. The Semester aims to tackle economic imbalances by giving European Union (EU) member states country-specific recommendations (CSRs) regarding their public budgets as well as their wider economic and social policies with a view to enabling better policy coordination among Euro Area member states. In this article we develop a method to assess the way in which the CSRs have been addressing coordination and offer a systematic analysis of the way they have been formulated. We offer a way to code CSRs as well as one to analyse progress evaluations. Furthermore, we seek to use our results to address one of the reoccurring questions in the literature: whether the EU is pursuing a ‘one size fits all’ approach to economic policy making in the Euro Area? The findings indicate that different types of market economies and welfare states – different ‘varieties of capitalism’ – among the Euro Area members obtain different recommendations regarding different policy areas

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)0.023
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.725
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0230.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.325
GPT teacher head0.415
Teacher spread0.090 · 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.

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

Citations38
Published2019
Admission routes2
Has abstractyes

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