Convergence tendencies in the EU Member States - a statistical study for the period 1980-2013
Bibliographic record
Abstract
The aim of the article is empirical analysis of convergence process in the European Union especially after extending it by the groups of less developed countries („cohesion” countries after their accession in the 1980s and Central and Eastern Europe countries after 2004). The econometric methods, based mainly on regression growth models, are implemented, first of all, to verify the hypothesis about the existence of beta convergence and its impact on sigma convergence; secondly, to verify the theoretically proved statement that capital accumulation become less important in convergence processes as compared to the increasing role of technological progress. The results of the investigation point at the existence of beta convergence and its important but decreasing impact on reducing income disparities among European Union Member States. An additional survey on the existence of convergence clubs, conducted using the approach based on polynomial functions, , confirms, that all the analysed countries were approaching the same steady state and creating a common convergence club. JEL Classification Code: G10, G15.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".