Gender inequality in urban water governance: Continuity and change in two towns of Nepal
Bibliographic record
Abstract
Abstract Gender‐based inequality has long been recognized as a challenge in water governance and urban development. Women do most of the water collection‐related tasks in the majority of low‐income country's urban areas, as they do in rural areas for drinking, household consumption, kitchen gardening, and farming. However, their voice is rarely heard in water governance. When climate change exacerbates water scarcity, it becomes harder for people to secure water with more pronounced effects on women. Drawing on the narratives of men and women involved in water management practices and also the views of the stakeholders who are part of water resource management in two towns in Nepal, this paper demonstrates emerging forms of gender inequality concerning access to and control over water resources, as well as associated services such as sanitation. We found that women's voice in water governance is systematically excluded, and such gender‐based disadvantage intersects with economic disadvantage as women in low‐income poor urban settlements are experiencing additional difficulty in accessing water and sanitation services. Gender inequity persists in the urban water sector, and of course the wider social structures, despite some progressive policy changes in recent years, such as the 30% quota reserved for women in local‐level water management bodies in Nepal. The paper concludes that tackling gender inequity in water management requires a transformative approach that seriously takes into account women's voice, critical awareness, and open deliberation over the causes and consequences of the current approaches and practices. Moreover, gender‐inclusive outcomes on water management are linked to changes in areas outside of the water sector, such as property ownership structures that constrain or enable women's access to water and related services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".