Which Road to Development? The Mediterranean Model Revisited
Bibliographic record
Abstract
This chapter lays down the focus of the book: Southern Europe, particularly Italy, Spain, Portugal, and Greece. It outlines the several good reasons why Southern Europe should be studied more carefully. This includes the region's relatively extensive contribution to European wealth. Italy and Spain are the third- and fourth-largest European Union economies in terms of gross domestic product, respectively, and around a quarter of EU citizens live in Southern Europe. Another reason cited was the importance of improving a more analytical understanding of capitalist models that are not usually studied. Then the chapter highlights that Southern Europe was not just the most severely hit EU region during the euro crisis which started in 2010, but also the one that experienced the most difficulty in recovering. The chapter underscores the unstable growth of the Mediterranean economies, compared to the Nordic and Central and Eastern European countries. Furthermore, it explores how to study Southern Europe's political economies using old and new analytical tools. It raises these two main questions that the book intends to answer: Why do Southern Europe economies share difficulties in being competitive and finding a stable growth pattern in the global economy? How likely is it that the four countries will follow different paths in the future and increasingly diverge?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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.
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".