Drivers of convergence in eleven eastern European countries
Bibliographic record
Abstract
This paper investigates the drivers of \n growth and prosperity in a group of eleven European \n countries -- Bulgaria, Croatia, the Czech Republic, Estonia, \n Hungary, Latvia, Lithuania, Poland, Romania, Slovenia, and \n Slovakia (the EU11). Since the EU11 began the transformation \n process, this group of emerging countries has made \n impressive strides as developing market economies and is \n anchoring development in European Union institutions. There \n are reasons to believe that the convergence of EU11 income \n per capita to Western European levels will continue, but \n will proceed more slowly. The paper concludes that trade and \n financial integration have sped along at a spectacular pace \n in the EU11 in the recent past, although trade in modern \n services and the integration of government bond and equity \n markets are somewhat behind. As in the rest of Europe, \n demographic developments will pose huge challenges for the \n sustainability of public finance in the EU11 economies. In \n the next several decades, the EU11 labor force is expected \n to contract more than labor forces in the rest of the \n European Union, making it even more urgent that countries in \n the region reform pension systems, change migration policy, \n and find incentives to attract talent to the region. Closing \n the gap with the rest of the European Union in educational \n attainment levels and improving education quality might \n significantly soften the constraints imposed by the \n demographic threats and produce sizable returns in terms of \n additional income convergence.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".