The Water‐Sensitive City: Implications of an urban water management paradigm and its globalization
Bibliographic record
Abstract
The urban water management (UWM) community is embracing a paradigm shift to tackle the escalating water stress experienced in several cities globally, as existing challenges are predicted to be exacerbated by climate change and population growth. The term “Water Sensitive City” (WSC) is widely used in literature to describe this new ideal to aim for, where cities will successfully deliver safe and reliable water services to all, now and in the future, in an eco‐friendly manner. This green, long‐term vision implies a large amount of stakeholder coordination and institutional support, as well as participatory community engagement. Examining the foundations and principles of the WSC as well as the experiences in its application, impacts and limitations, particularly in the Global South, provides a space to further contribute to the dynamic field of UWM and governance. This article provides a condensed overview of the WSC approach and related emerging conversations in the urban water sector across a range of disciplines: the objective is to nurture reflection from young professionals entering the field of UWM, but also to offer an opportunity for more experienced scholars and practitioners to take a step back in considering this rising approach from different perspectives. This article is categorized under: Engineering Water > Sustainable Engineering of Water Engineering Water > Water, Health, and Sanitation Engineering Water > Planning Water
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.073 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.001 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".