Learning sustainable water practices through participatory irrigation management in <scp>T</scp>hailand
Bibliographic record
Abstract
Abstract Participatory irrigation management (PIM) was adopted in Thailand in 2004 to encourage the sustainable use of water in the agricultural sector. The research presented in this paper sought to understand the relationships between public participation, learning, and the implementation of more sustainable water practices through PIM in Thailand. Data was collected through document reviews, observation, informal meetings, and a total of 55 semi‐structured face‐to‐face interviews of local irrigators from two case study regions around the Krasiew Reservoir. Results showed that participating in PIM activities facilitated both instrumental (e.g., water supply and demand data, benefits of on‐time water delivery) and communicative (e.g., reasons for past PIM failure, expectations of fellow farmers) learning among PIM participants. Findings also revealed that social action is fostered through the recognition of human dignity and compassionate communication that instils a sense of ownership and solidarity among irrigators. Sustainable water practices among local farmers were spurred further through learning that the reservoir is a finite water source.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".