Are Structural Funds a Real Solution for Regional Development in the European Union? A Study on the Northeast Region of Romania
Bibliographic record
Abstract
Economic development has been a major priority for the European Commission, with significant amounts of Structural and Cohesion Funds being allocated in this direction. With the enlargements of the Union in 2004, 2007 and 2013, the Regional Development Policy faced a new challenge, with the disparity between new members and the community average being a notable one. The literature is divided with respect to the impact generated by funds allocated through the Regional Development Policy, as some authors claim the existence of positive effects, others identify conditional positive effects and other authors identify only negative effects and say that the whole support system needs to be rethought. This research presents an empirical approach to the issue of the effectiveness of the European Community’s support system for business environments. An analysis is performed at the microeconomic level in order to quantify observable effects at the level of the SMEs that have benefited from non-reimbursable financial aid. The data obtained indicate that Structural and Cohesion Funds for business environments have a significant effect in the medium and long terms, contributing to the achievement of the general objective of the Regional Development Policy (reducing economic disparities between EU member states).
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".