Half Empty or Half Full? Over 30 Years of Regional Cross-Border Cooperation Within the EU: Experiences at the Dutch–German and Danish–German Border
Bibliographic record
Abstract
This article examines four cross-border regions across the Dutch–German and Danish–German border, which now have experience of over 30 years' more or less institutionalized cross-border cooperation: the Euregio Rhein–Ems–IJssel (often referred to as EUREGIO), the Ems–Dollart Region, Region Sønderjylland-Schleswig, and Fehmarn Belt Region. The focus is on evaluating barriers against and incentives for cross-border activities in connection with cross-border regional governance. While cross-border cooperation in the form of euroregions today is a common sight on virtually all European borders, research still has limited knowledge on mechanisms involved with cross-border region-building as well as on opportunities for successful cross-border cooperation. The article reveals that the four regions examined, though very different in socio-economic characteristics, cooperation history, and extent, face comparable issues and problems in cross-border cooperation. It shows that the “difference of attractiveness” of each side of the border is a decisive impetus to engage in cross-border activities, providing that “information” on the conditions for cooperation as a decisive variable is available or can be made available through third-party funding such as the Interreg Community Initiative. A lack of “difference of attractiveness” requires third-party funding for sustainable cross-border activities. The euroregions' and their institutions' role is predominantly the role of a cross-border information center, network organizer, and support organization, while their actual governance of self-sustainable cross-border activities remains low.
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.004 |
| 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".