Urban Water Security: Definition and Assessment Framework
Bibliographic record
Abstract
Achieving urban water security is a major challenge for many countries. While several studies have assessed water security at a regional level, many studies have also emphasized the lack of assessment of water security and application of measures to achieve it at the urban level. Recent studies that have focused on measuring urban water security are not holistic, and there is still no agreed-upon understanding of how to operationalize and identify an assessment framework to measure the current state and dynamics of water security. At present, there is also no clearly defined and widely endorsed definition of urban water security. To address this challenge, this study provides a systematic approach to better understand urban water security, with a working definition and an assessment framework to be applied in peri-urban and urban areas. The proposed working definition of urban water security is based on the United Nations (UN) sustainable development goal on water and sanitation and the human rights on water and sanitation. It captures issues of urban-level technical, environmental, and socio-economic indicators that emphasize credibility, legitimacy, and salience. The assessment framework depends on four main dimensions to achieve urban water security: Drinking water and human beings, ecosystem, climate change and water-related hazards, and socio-economic factors (DECS). The framework further enables the analysis of relationships and trade-off between urbanization and water security, as well as between DECS indicators. Applying this framework will help governments, policy-makers, and water stakeholders to target scant resources more effectively and sustainably. The study reveals that achieving urban water security requires a holistic and integrated approach with collaborative stakeholders to provide a meaningful way to improve understanding and managing urban water security.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".