Gender and Community Mainstreaming in Fog Water Collection Systems
Bibliographic record
Abstract
Fog water collection is an emerging opportunity to combat local water shortages in water-scarce areas where sustainable access to water is unreliable, but fog events are frequent. Since fog water systems are implemented within or near communities, they eliminate or decrease the need to travel far distances for the collection of water during times of scarcity. As a result, these systems decrease the physical and social burden of water collection on women and girls, who are the primary water gatherers in most traditional communities. This is an important outcome because women and girls are disproportionately affected by water scarcity and are not seen as equals in water management, access, or control. This paper illustrates how several fog water collection projects have shown, empirically, that the positive outcomes for women and girls may include the freeing of time for domestic and educational pursuits, improved health outcomes, and improved perceptions of self and others’ perceptions of women. These findings are important at a time when the world at large is addressing the Sustainable Development Agenda, where Sustainable Development Goal (SDG) 6 necessitates safe water and sanitation for all and SDG 5 ensures gender equality to empower all women and girls.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".