Rural water sustainability index (RWSI): an innovative multicriteria and participative approach for rural communities
Bibliographic record
Abstract
This paper proposes a Rural Water Sustainability Index (RWSI). Using this tool, decision-makers can identify and prioritize locations that require state intervention to develop strategies and guarantee water to rural communities. Multi-criteria analysis (MCA) and Geographical Information System (GIS) were combined to integrate different indicators into the assessment and generate maps showing spatial levels of water sustainability in rural communities. RWSI was applied on a case study in 26 rural communities in the municipality of Pombal, Paraíba, Brazil. We realized 165 interviews with those living in rural communities. Consultation with experts was conducted using the Delphi method to assign weights and scores to the components, subcomponents, and indicators. The results illustrated a heterogeneous spatial behavior between rural communities of the municipality of Pombal, even though the index values for the majority (57.7%) of communities ranged from 5.8 to 6.0. For application in other countries and regions, researchers need to conduct public and expert consultation to adjust weight of components and subcomponents, and then the RWSI method can estimate water sustainability and produce maps anywhere in the world.
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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 | 0.033 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.009 |
| 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".