Water scarcity and excess: water insecurity in cities of Nepal
Bibliographic record
Abstract
Abstract The world is facing the greatest and most complex twin challenges of water insecurity: scarcity and excess, with their adverse consequences on health, well-being, and developmental outcomes. Against this backdrop, we analyzed the challenges households face due to ‘too much and too little water’. The research employed a qualitative methodology in which data were collected through 40 key informant interviews, informal conversations, and observations during 2020–2021 including a relevant literature review. We note that both ‘too much and too little water’ pose risks to water insecurity. Also, water security cannot be ensured by only dealing with water inadequacy without building a resilient water system and robust institutions. We found that water scarcity has affected other components of water security such as equity, quality, and affordability. Excess water has impacted water infrastructures, degrading the water quality, and risking human health and well-being. The responses to the water challenges were hindered by several constraints such as the limited capacity of the water institutions, frequent leadership changes, political influence, and emerging challenges in the federal context. We suggest timely planning and adopting site-specific innovations to address water scarcity and excess challenges, which include strengthening water services, infrastructures, institutions, and governance.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".