Collaborative Problem-Solving in the Cross-Border Context: Learning from Paired Local Communities along the Russian Border
Bibliographic record
Abstract
The article investigates governance structure and information sharing as managerial tools utilized by borderland communities to solve local problems coordinately. Focusing on three case studies of transfrontier intermunicipal cooperation on the Russian-Norwegian, Russian-Finnish and Russian-Chinese borders gave a chance to illustrate that selected instruments provide heterogeneous results in the cross-border context in terms of dependence on socio-economic and cultural circumstances of each locus and in the process of public value creation. Testing hypotheses revealed that governance structures of adjacent border municipalities tend to adjust to each other regardless the milieu, as well as majority of local mass media, tend to initiate collaboration with similar organizations across the border. However, these initiatives frequently remain unsuccessful as information sharing is a region-specific variable that relies on local communication culture, understanding of mass media mission and information production. Applying a ranging technique allowed visualization of carried out research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".