Towards sustainable urban sanitation: a capacity-building approach to wastewater mapping for small towns in India
Bibliographic record
Abstract
Abstract Decentralized technologies and city-based governance are being actively promoted for urban sanitation in low-income countries. At the same time, municipal agencies in developing countries have little technical or financial capacity for sanitation planning. This paper develops an approach to sanitation planning that leverages citizen engagement and fosters local capacities. It presents an empirical study from two small towns in India, where collaborations among the research team, local academics and students, and the municipal government, produced planning-oriented sanitary maps of each town. The maps were built upon a social and spatial understanding of the diverse sanitation practices that already exist, coupled with Google Earth and free GIS software. The ‘waste watersheds’ and ‘sanitation zones’ identified through the mapping process provide a basis on which sanitation interventions can be assessed and weighed, so that sustainable solutions can be prioritized. The paper identifies three features for system interventions: first, making local municipal government the locus of sanitation interventions; second, engaging community-based organizations and academic institutions to develop local capacity; and finally, recognizing the fragmented nature of cities by developing a socio-spatial approach to sanitation zoning.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| 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".