Two Regions, One Vision: The Cross-Border Mission of the Via Carpatia EGTC
Bibliographic record
Abstract
This article illustrates how the organization European Grouping of Territorial Cooperation Via Carpatia (from here on referred to as Via Carpatia EGTC) with limited liability exemplifies the potential of EGTCs to act as effective platforms for overcoming administrative borders and fostering meaningful, results-oriented cooperation that responds to local needs while aligning with broader EU objectives. The Via Carpatia EGTC has played an important role since 2013 in promoting cross-border cooperation between Slovakia’s Košice Self-governing Region and Hungary’s Borsod–Abaúj–Zemplén County. Its activities contribute to regional development through coordinated projects and initiatives focused on infrastructure, culture, and economic growth. The Via Carpatia EGTC serves as a key policy instrument for fostering territorial cohesion, addressing regional disparities, and enhancing cooperation in various thematic areas including education, ecology, social policy, and infrastructure. A significant component of Via Carpatia EGTC’s work is the Small Project Fund (SPF), through which numerous local initiatives such as cultural events, sports programs, and support for regional producers have been financed. In this article, we also highlight several international collaborations aimed at improving digitalization in elderly care, promoting environmental awareness among youth, and advancing circular economy models. Since 2013, Via Carpatia EGTC has mobilized more than €19 million in investments, contributed to the creation and support of over 1,100 jobs, and implemented more than 40 successful projects. Key words: cross-border cooperation, European Grouping of Territorial Cooperation, small project fund, regional development, interregional partnership, building partnerships.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".