Participatory Research Revealing the Work and Occupational Health Hazards of Cooperative Recyclers in Brazil
Bibliographic record
Abstract
Although informal waste collectors are sometimes organized in cooperatives, their working conditions remain extremely precarious and unsafe. The paper discusses the findings of action oriented, participatory qualitative research with several recycling groups in the metropolitan region of São Paulo, Brazil. During workshops with the recyclers mapping, acting, and drawing methods helped reveal health hazards from collection, separation and transportation of recyclable materials. Major health problems relate to chemical and biological hazards, musculoskeletal damage, mechanical trauma and poor emotional wellbeing. The recent federal legislation on solid waste management opens new avenues for the inclusion of recycling cooperatives in selective waste collection. Nevertheless, we express the need to consider the distinctive characteristics and vulnerabilities of recycling groups, when developing safer work environments in these social businesses. We also suggest that the workspace be ergonomically organized and that public awareness campaigns about selective waste collection are conducted regularly to increase the quality of source separation. The introduction of electric hand pushed carts can further reduce health strains. This research has produced a better understanding of the work of the recyclers and related health risks. The interactive qualitative research methodology has allowed for the co-creation and mobilization of specific knowledge on health and safety in recycling cooperatives.
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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 | 0.020 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".