Inequity in water distribution and quality: A study of mid‐hill town of Nepal
Bibliographic record
Abstract
Abstract Himalayan cities are highly vulnerable to climate change and increasingly exposed to water insecurity. Given the complexities of water usage within society, developing, allocating, and managing water resources equitably is a serious emerging challenge. This paper adopts a convergent mixed method approach to explore water inequity issues in water distribution and water quality among the core and peripheral wards of Dhulikhel, a mid‐hill town of Nepal. In doing so, the paper analyzes the determinants of inequitable water distribution in core and periphery wards, perception of water quality, and underlying causes of inequity in water quality in core and peripheral wards. The paper found that various socioeconomic, environmental, technological, and governance‐related factors are causing inequity in water distribution. Our analysis showed that climate change is adding on top of these existing challenges, exacerbating inequity in access to water. This paper also found that while core wards benefit from donor schemes that ensure good water quality, the peripheral wards do not enjoy the reach of such schemes, and given climate change impacts on rainfall patterns, seasonal availability of water is likely to be unpredictable in the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".