Business Networks in the Cross-border Regions of the Enlarged EU: What do we know in the Post-enlargement Era?
Bibliographic record
Abstract
In the context of the Eastern European enlargement, locally based networks of enterprises were expected to act as an important driver of economic integration of the cross-border regions. In a globalized world, this type of network is supposed to play a vital role in strengthening the competitiveness of the peripheral regions along the former political–economic frontier between Western and Eastern Europe. In practice, however, only weak network-building across borders was observed in many of the border areas, instead, that involves local enterprises. The contrasting picture of the theoretical propositions and the empirical evidence is the starting point for the present paper. Its aim is to present the key insights into the issue of business networks in the cross-border regions in a post-enlargement era. It proceeds along the following lines: after having confronted the theoretical propositions on the topic with empirical evidence, a case study of a historically integrated cross-border network underpins this literature overview with primary data and highlights the perspectives and limitations of the business networking potential for a case region. The article finishes by sketching a research agenda that aims at reconciling the different views on the development of cross-border business networks and calls for new empirical research.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.019 |
| Open science | 0.001 | 0.003 |
| 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".