Alternative Interventions to Assist Farmer-Managed Irrigation Systems in Nepal
Bibliographic record
Abstract
"This paper examines the consequences of various types and levels of \ninterventions in Farmer Managed Irrigation Systems (FMIS) in Nepal. Systematic and comparative analysis of 13 FMIS interventions by 13 different agencies in the hill districts of Nepal tries to answer the question of how and why some ways of helping FMIS have had positive results and others have had no or negative effects. The important variables include intervening agencies, their process to select particular system, assistance objectives of programs, cost sharing \ncriteria, mode and basis of resource mobilization, and changes in \nagricultural performances due to intervention. Then the paper documents the process of intervention and examines performances of 19 irrigation systems in one hill district intervened by one of the intervening agency - Water and Energy Commission Secretariat/International Irrigation Management Institute-Nepal (WECS/IIMI). After the initial discussion of WECS/IIMI action research agenda, the paper discusses the methods of selection of systems for intervention, brief description of the selected Irrigation systems; and documentation of farmer-to- \nfarmer training process during intervention. Finally, performance of these 19 systems are compared before and after Intervention based on the analysis of changes in technical efficiency, organizational structure, resource mobilization, rules, and agricultural productivity."
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".