The European Regional Development Fund and Island Regions: An Evaluation of the 2000-06 and 2007-13 Programs
Bibliographic record
Abstract
This paper presents results from a regional policy evaluation study conducted for the European Commission. The study examined the impact of the European Regional Development Fund and Cohesion Fund on EU regions with ‘specific geographical characteristics’, namely islands, mountain regions and sparsely populated areas. These types of regions have been attracting increasing EU regional policy attention and their economic development is considered important in helping the EU to attain its important ‘territorial cohesion’ objective. The focus of this paper is on the island regions. Evaluation of island regions in their own right has not been undertaken before by the EU. The study focuses on the 2000-06 and (still on-going) 2007-13 EU regional policy programs. The paper presents the methodology adopted by the study before turning to the main findings concerning the types of policy initiatives adopted in the island regions, and the appropriateness of the policies used for the economic situation faced by the islands. The islands encompassed by the study are all normal sub-national regions of EU member states. Islands with an unusual degree of administrative autonomy (e.g. the Outermost Regions) were excluded.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.063 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".