Economic and Monetary Union at Twenty: A Stocktaking of a Tumultuous Second Decade
Bibliographic record
Abstract
The contributions to this book examine the two main asymmetries of the Euro Area as they have intensified during the second decade of Economic and Monetary Union (EMU): the first between monetary union (more supranational governance) versus ‘economic’ union (less centralised governance); the second between those Euro Area member states of the so-called ‘core’ and those of the ‘periphery’. EMU stands as one of the European Union’s (EU) flagship integration achievements. Set up in 1999, with the large majority of EU member states at the time, EMU was described as ‘asymmetrical’ even prior to its start. From the outset, it involved asymmetrical integration in monetary and ‘economic’ union. Although a major element of the blueprint that paved the way for the final stage of EMU, the concept of ‘economic’ union was insufficiently developed. The second decade of the single currency gave rise to a second asymmetry, namely one between those Euro Area member states of the ‘core’ and those of the ‘periphery’. The ten contributions to this volume speak to one or both of these asymmetries, covering the major political, political economy and policy dimensions of EMU and the ongoing debates about necessary policy and institutional reforms to overcome these asymmetries and bolster Euro Area stability. The outbreak of the Coronavirus (Covid-19) Crisis in 2020 created unprecedented socio-economic challenges for Euro Area member states, heightening the perceived urgency of reform.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".