Collaborative Governance for Sustainable Development in Nepal: Lessons from Large-Scale Infrastructure Projects
Bibliographic record
Abstract
This study investigates the role of collaborative governance in promoting sustainable development in Nepal by analyzing two large-scale infrastructure projects: a prominent hydropower company and a major water supply project. Using thematic analysis, the study involved coding, categorization, and theme identification through twelve interviews with key stakeholders. The findings highlight significant gaps in the governance structures of these projects, particularly in terms of resilience, equality, and well-being. Despite achieving technical success, both projects failed to adequately address broader social and environmental goals, leading to the marginalization of local communities and a decline in their well-being. The study is limited to these two projects and does not account for governance frameworks in other sectors or smaller-scale projects. Future research could expand by studying additional infrastructure projects or comparing governance practices in similar economies, thereby enhancing the potential to generalize the findings. The study addresses equity and inclusivity in collaborative governance for infrastructure development a relatively underexplored area in Nepal, emphasizing the importance of inclusive and equitable governance frameworks to achieve sustainable growth in underdeveloped economies.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".