Household waste and health risks affecting waste pickers and the environment in low- and middle-income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Household waste has evolved into a core urban challenge, with increased quantities of waste being generated and with more complex material compositions, often containing toxic and hazardous elements. Critical systems theory understands cities as urban metabolisms, with different material and energy flows, highlighting the circularity in production, consumption, and discard. Waste pickers in low- and medium-income countries work on dumps and landfills, sifting through highly contaminated household waste and are exposed to health hazards. This paper discusses the risk factors, hazards, and vulnerabilities waste pickers are exposed to during collection and separation of recyclables, based on the review of literature on waste and environmental health and on findings from participatory research with waste pickers conducted in Brazil. We take a social and environmental justice perspective and identify the vulnerabilities and waste-borne hazards of household waste, associated with these workers, their communities, watersheds, and the environment. Household waste, although not always per se toxic or hazardous, can become a hazard if not collected or inadequately managed. Those communities where household waste is not collected or waste collection is insufficient are the most critical places. Informal and organized waste pickers, municipal or private waste collectors/workers, small waste traders and sometimes residents, particularly small children, may be considered vulnerable if exposed to waste-borne hazards. The results include recommendations to address household waste-borne hazards and vulnerabilities, according to waste workers involved in this research.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it