Towards characterizing the adaptive capacity of farmer-managed irrigation systems: learnings from Nepal
Why this work is in the frame
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Bibliographic record
Abstract
Small-scale irrigation systems managed by farmers are facing multiple challenges including competing water demand, climatic variability and change, and socioeconomic transformation. Though the relevant institutions for irrigation management have developed coping and adaptation mechanisms, the intensity and frequency of the changes have weakened their institutional adaptive capacity. Using case examples mostly from Nepal, this paper studies the interconnections between seven key dimensions of adaptive capacity: the five capitals (human, financial, natural, social, and physical), governance, and learning. Long-term adaptation requires harnessing the synergies and tradeoffs between generic adaptive capacity that fosters broader development goals and specific adaptive capacity that strengthens climate-risk management. Measuring and addressing the interrelations among the seven adaptive-capacity dimensions aids in strengthening the long term sustainability of farmer-managed irrigation systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it