End-user participation in drinking water management through Awareness, Education, and Resources: Tools for Water Partnerships
Bibliographic record
Abstract
ABSTRACT The long-term success of safely managed drinking water systems requires participation from many stakeholders, especially the water users, but also community leaders, technical experts, health practitioners, teachers, NGOs, and others. However, people may need to be empowered before they can be confident or able to contribute to meaningful decision making or governance roles in Water Partnerships. Discussions with water leaders from nine organizations on four continents revealed a surprisingly similar three-step approach to empowering end-user participation in water management. The first step is creating the Awareness of local water challenges and their impact on health. The second step is Education about the options for safe water. Together, Awareness + Education lead to people understanding their challenges and being able to devise a locally appropriate solution. The third step is Resources for water action. Through Awareness, Education, and Resources, water stakeholders including households can be empowered to participate in safely managing drinking water solutions.
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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 | 0.024 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".