Community engagement in water management to enhance sustainability: A case study of Bangkachao, Thailand
Bibliographic record
Abstract
Bangkachao, the important and largest greenspace community near Bangkok, has been threatened by several water issues such as increasing salinity, polluted water, and floods. The study conducted a participatory action research (PAR) project on water management with community participation, aiming to solve these problems from the bottom up. The research shows that community engagement in the management of water resources can enhance sustainability. The collective efforts of the research group and local stakeholders proved very effective in resolving water management issues and creating knowledge. They aided in knowledge sharing during and beyond the project period. The evidence-based argument is essential for making change. Tangible results include a significant change in the main watergate management to solve water problems and the creation of an accurate map of existing canals and water gates around the island, which support local understanding of water management and regular monitoring enhanced by statistical data and technical tools. Human and social capital gains continue to be seen in longer-term work and continued efforts to monitor water problems.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".