Establishment of Transboundary Partnerships in an International Climate Adaptation Project
Bibliographic record
Abstract
The transboundary partnership encourages multi-stakeholder efforts to mobilize adaptation funding and services for adaptation governance. However, challenges exist in scope, equality, and transparency when engaging these stakeholders. Few studies have examined the detailed coordination among multi-stakeholders and the performance of their partnerships, creating an opportunity to understand how multi-stakeholders contribute to and manage efforts for future adaptation projects. This study focused on a transboundary action research project on climate adaptation, analyzing the partnership structure, stakeholder arrangements, coordination scenarios, and the role of the partnership within it. It included interviews with project personnel and focus group discussions with community members at the project sites. The results showed that the project emphasized collaboration while maintaining both vertical and horizontal coordination. The donor maintained vertical coordination for monitoring but collaborated on budget adjustments, funding, identifying challenges, and developing strategies to improve the partnership. Partners continued horizontal coordination by sharing responsibilities and taking leadership roles in synthesizing research reports. Additionally, this project facilitated community participation in piloting the adaptation intervention. Although this partnership aimed to establish collaboration, it remains far from effectively liaising with national-level governments to maximize the benefits of adaptation technology. Future research should expand this scope to promote sustainable development.
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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 | 0.027 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.001 | 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".