Bibliographic record
Abstract
This paper compares the benefits to Greece, the Euro zone and the rest of the world arising from policies that prevent a Greek default and exit from the Euro with the costs of preventive policies. It concludes that the benefits exceed the costs, though unpredictable politics and nationalist aspirations may prevent the adoption of the rational policies. The paper also considers the causes of Greece’s problems: the failure of lenders to ask for a proper risk premium on the country’s bonds; Greece’s publication of false economic data; the failure of credit rating agencies to down-grade its bonds; the global financial euphoria and supply of liquidity that made lenders disregard traditional standards in all their dealings. The paper recommends policies to ensure the proper functioning of financial markets to prevent future crises. Key words: Greece bankruptcy, Euro survival, Greek statistics, Credit ratings. JEL Classification: F33, F34, F36, F55, G15, G24. Resumen: Este artículo compara los beneficios con los costes derivados del salvamento griego, llegando a la conclusión de que los beneficios superan claramente a los costes. También se analizan las causas del problema, el papel de las sociedades de rating y la euforia previa especulativa, efectuán-dose unas consideraciones sobre el futuro del euro y del orden financiero internacional. Palabras clave: Bancarrota Griega, Euro, Estadísticas en Grecia, Credit ratings. Clasificación JEL: F33, F34, F36, F55, G15, G24.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".