Alternative Interventions to Assist Farmer-Managed Irrigation Systems in Nepal
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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