A border regions typology in the enlarged European Union
Bibliographic record
Abstract
The processes of European Union (EU) integration and enlargement have produced a new regional socioeconomic map in Europe. Border regions, in particular, have been put in a state of flux. The re‐allocation of activities, opportunities and threats is changing their socioeconomic role and significance. Thus, border regions have become an issue of great importance during the last fifteen years in both the areas of scientific research and policy making. The overall picture of the actual dynamics occurring at the border regions, however, when economic barriers have been abolished, remains rather unclear. The absence of an appropriate methodological framework for the study of the impact of EU integration and enlargement dynamics on border regions is evident. The paper proposes a typology for the EU NUTS III border regions, interpreting the socioeconomic dynamics occurring within the enlarged EU space. Primary and secondary data, incorporating quantitative and qualitative determinants for border regions, were elaborated with integrated factor and fuzzy clustering analysis techniques. The proposed border regions typology provides a framework to assess the relative position of each EU border region in the EU space.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".