Bibliographic record
Abstract
With Economic and Monetary Union (EMU) the European Union is embarked on a major historic political project of formidable technical complexity. In January 2009 the Euro Area will be ten years old. What does the evidence from the first decade tell us about the significance of the euro for the EU and its member states? This book brings together a range of recognized academic specialists to examine the main political aspects of this question. How, and in what ways, has the euro Europeanized states (members and non-members), their institutions, policies and politics? What have been its effects on the location and use of power? Has the euro generated convergence or divergence? What political patterns can be identified? The book offers the first, in-depth and systematic political analysis of the first decade of the euro. It places the euro in its global and European contexts; offers a set of case studies of its effects on a representative sample of EU member states ('Anglo-Saxon', old 'D-Mark Zone', east central European and Baltic, Mediterranean, and Nordic); and looks at three key sectors (financial markets, wages and collective bargaining, and welfare reform). The book contributes to Europeanization studies, comparative political economy, and studies of Economic and Monetary Union (EMU). It will be of major interest to students of the European Union and European integration, comparative European politics, and area and 'country' studies.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".