Cross-Border Cooperation in the Carpathian Euroregion: Ukraine and the EU
Bibliographic record
Abstract
Cross-border cooperation among the Eastern neighbours of the European Union can be understood as a new approach to public policy and border governance in the region. There was no border cooperation strategy between communist and European countries during Soviet times. The question of the management of the Eastern border of the EU, especially with Belarus, Ukraine, and Moldova, came on the agenda in 1997, when accession to the union was finally opened to Eastern and Southern European candidates. With the Partnership and Cooperation Agreement that came into force in 1998, Ukraine signalled its foreign policy orientation as European, asserting that Western integration would help modernize its economy, increase living standards, and strengthen democracy and rule of law. The European Commission required “good neighbourly relations” as a further condition for accession and in conjunction, the concept of “Wider Europe” was proposed to set up border-transcending tasks. The Carpathian Euroregion was established to contribute to strengthening the friendship and prosperity of the countries of this region. However, the model was not fully understood and had only limited support of the national governments. This article uses the Carpathian Euroregion as a case study to show that overall Ukraine and the EU’s Eastern neighbourhood presents more opportunities for effective cooperation with the EU rather than barriers or risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".