Cross-Border Institutional Trust in Post-Pandemic Times. The Role of EU <i>b-solutions</i> Initiative
Bibliographic record
Abstract
Mutual trust is an important element in starting cross-border processes, and institutional trust is vital to forging sound and effective cross-border cooperation. It is widely debated that the recent covidfencing process in Europe chipped away existing long and medium-term cross-border institutional ties, in most European borders, at least for some time. The fundamental point is that the sudden closing of national borders also revealed how important are effective and well-functioning cross-border institutions to direct the reopening of such borders into functional cross-border areas. To appreciate more in-depth the importance of cross-border institutional trust in post-pandemic times, this article analyses to what extent the EU b-solutions initiative contributes to reinforcing it along European cross-border regions. The analysis concluded that this EU initiative reinforces cross-border institutional trust by enhancing institutional capacity building, institutional knowledge sharing and stability/credibility as well as territorial integration in EU border regions. However, only a few European cross-border regions have benefited the most from this institutional support by b-solutions: the Benelux, plus Germany and France, as well as the Iberian cross-border areas.
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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 | 0.013 |
| 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 | 0.009 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.008 |
| 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".