Role of Resource Asymmetry and Collaboration Time in the Governance of Cross-border Collaborative Networks
Bibliographic record
Abstract
This study aimed to analyze the micro-governance of a cross-border Brazil–Uruguay collaborative network and how it contributes to a favorable environment for collaboration. To achieve this objective, a single case study was conducted with a qualitative approach using document analysis and interviews with 19 Brazilian and Uruguayan stakeholders from the public, private, and civil society sectors. The results show that two contextual factors, resource asymmetry among participants and collaboration time, explain the need for greater emphasis on the use of certain governance functions while, at the same time, making other functions less necessary. The behaviors become more predictable and stable with the collaboration time, requiring less emphasis on alignment, monitoring, and conflict arbitration in collaborative networks. However, resource asymmetry requires efforts to integrate, mobilize, and organize the participants and their resources. We contribute to the theory by showing how contextual factors affect the use of micro-governance functions in a cross-border collaborative network and how the governance fosters an environment that stimulates collaboration. Practitioners can also benefit from our study, as it facilitates a deeper comprehension of how collaborative networks can be governed to produce collective results in cross-border contexts.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".