European Regional Inequalities: The Other Face of Development
Bibliographic record
Abstract
Empirical evidence suggests an overall convergence in terms of GDP and per capita income occurring among the European Union (EU) Member States. Nevertheless, economic inequalities have been increasing at the regional level within European Union countries. Through the review of relevant literature, this study analyzes the increasing inequalities from an economical point of view, focusing on Italy and the UK as examples. First, a general overlook of the empirical evidence of the GDP and per capita income at national and sub-national levels will be presented. Second, an explanation of the possible causes of the results will be proposed through the use of economical and sociological theories. The findings of this research might uncover the relative inefficacy of EU Cohesion policies and point towards the necessity for deeper and more thoughtful measures to continue the convergence of Member States while preserving internal equilibria. This paper ends with discussions for the future directions of the EU.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".