Social Hydrological Analysis for Poverty Reduction in Community-Managed Water Resources Systems in Cambodia
Bibliographic record
Abstract
Achieving sustainable water resources management objectives can work in tandem with poverty reduction efforts. This study evidenced the strong social hydrological linkages that exist in Cambodia, which allowed for presenting a broader understanding of water resources challenges to better formulate and connect policies at the local and national levels. Models are often not developed with household- or community-level input, but rather with national- or coarse-level datasets. The method used in this study consisted of linking qualitative and quantitative social analysis with a previously developed technical water planning model. The results from the social inequalities analysis were examined for three water use types: domestic, rice production, and fishing in three parts of the watershed, namely, upstream, midstream, and downstream. Knowledge generated from the social analysis was used to refine previous water planning modeling. The model results indicate that without household data to consider social inequalities, the technical analysis for the Stung Chinit watershed was largely underrepresenting the shortages in irrigation supply seen by groups in the most downstream sections of the irrigation system. Without adding social considerations into the model, new policies or water infrastructure development suggested by the model could reinforce existing inequalities.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".