Gender dimensions of water vending in LMICs: A scoping review
Bibliographic record
Abstract
• Water vending is an important feature of water supply in LMICs. • Little is known about the gendered aspects and impacts of water vending. • Water vending is a gendered activity shaped by gender norms, roles and relations. • Water vending has the potential to contribute to water security and gender equity. This scoping review draws together the existing literature on the gender dimensions of water vending. Although research on this topic remains limited, available studies indicate that gender significantly influences the dynamics of water vending and its implications for gender equality. The expression of gender through water vending is context-specific, shaped by cultural, social, economic, and environmental factors, and it evolves over time. The findings show that gender norms, roles, and relations play a crucial role in shaping local water vending systems. Key factors that affect the relationship between water vending and gender include different types of labor—particularly the intersection of productive and reproductive work—and the broader economic, social, and environmental conditions in which water vending occurs. These findings highlight the need to recognize the gendered nature of water supply systems in order to ensure equitable water access and promote gender equality. This review emphasizes that, despite the heterogeneity of local water vending practices, the gendered nature of these activities remains a critical factor influencing broader issues of inequality.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".