Occupational health hazards faced by female waste-picking children in urban India: a case study of Bangalore City
Bibliographic record
Abstract
The paper presents the results of an investigation into the health hazards faced by waste picking children in Bangalore, the capital of the state of Karnataka in South India. The city of five million, has experienced phenomenal growth in recent decades as a result of large migration from rural parts of the state as well as neighbouring states. Most of these migrants fail to find paid work and end up in the sizeable informal sector, trying to eke out a living through self–employment. Many of the least skilled take up waste picking as their primary or supplementary occupation. Waste picking (WP) children often supplement the family income, but at times may be the major breadwinner of the family. Most of the WP children belong to the poorest families from among the lowest castes. They invariably live in slums in highly unhygienic conditions. Households in the city produce about 2,200 metric tons of solid waste daily, of which only about 80% is cleared. The waste is deposited in concrete bins on city streets for pick up by city trucks. Before the waste is picked up, the waste pickers rummage through the garbage looking for recyclable materials such as paper, plastics, metals and glass. The retrieved materials are then sold to small neighbourhood retail waste buyers. In view of the tropical conditions in the city, the waste in the bins putrefies quickly, attracting rodents, stray cats, dogs and cats as well as disease spreading vectors like flies, mosquitoes and cockroaches, making it a great health risk for waste pickers. While picking waste, the children almost never use any protection for their hands and feet or for breathing. We find in the study that, even when compared to other slum dwelling children, WP children have a significantly higher incidence of gastrointestinal, dermatological and respiratory diseases, in addition to the severe nutritional deficiency suffered by all slum children. This situation points up the need for targeted programmes for the prevention and treatment of the health problems as well as the general social and economic problems of waste pickers who perform the very useful social function of reduction and recycling of waste.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".