Measuring Economic Reform Recommendations under the European Semester: ‘One Size Fits All’ or Tailoring to Member States?
Bibliographic record
Abstract
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
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.112 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".