Health, hygiene and appropriate sanitation: experiences and perceptions of the urban poor
Bibliographic record
Abstract
“Don’t teach us what is sanitation and hygiene.” This quote from Maqbul, a middle-aged male resident in Modher Bosti, a slum in Dhaka city, summed up the frustration of many people living in urban poverty to ongoing sanitation and hygiene programmes. In the light of their experiences, such programmes provide “inappropriate sanitation”, or demand personal investments in situations of highly insecure tenure, and/or teach “hygiene practices” that relate neither to local beliefs nor to the ground realities of a complex urban poverty. A three-year ethnographic study in Chittagong, Dhaka, Nairobi and Hyderabad illustrated that excreta disposal systems, packaged and delivered as low-cost “safe sanitation”, do not match the sanitation needs of a very diverse group of urban men, women and children. It is of little surprise that the delivered systems are neither appropriate nor used, and are not sustained beyond the life of the projects. This mismatch, far more than an assumed lack of user demand for sanitation, contributes to the elusiveness of the goal of sanitation and health for all. The analysis indicates that unless and until the technical, financial and ethical discrepancies relating to sanitation for the urban poor are resolved, there is little reason to celebrate the recent global declaration on the human right to water and sanitation and health for all.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.011 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".