Characteristics, challenges and innovations of waste picker organizations: A comparative perspective between Latin American and East African countries
Bibliographic record
Abstract
Waste picker organisations (WPOs) around the globe collect, transport and process waste to earn their living but represent a widely excluded, marginalised and impoverished segment of society. WPOs are highly innovative, created by grassroots out of "nothing" to deliver economic, social and environmental sustainability. Still, we do not know how such innovations are developed, and how they are disseminated and adopted by other groups. This article examines characteristics, challenges and innovations of WPOs across five countries in Latin America and East Africa. It is based on quantitative and qualitative data regarding modes of organisation and management, gender, received support, business orientations, environmental and social contributions, and innovations developed in response to multiple challenges. The paper provides a comprehensive understanding of WPOs' activities and their grassroots innovations in the Global South. The study shows how WPOs contribute significantly to the economic, social and environmental sustainability of the societies they serve as well as the wider urban societies. To start and maintain WPOs in informal settlements with a lack of infrastructure, institutional frameworks, and public and private investors is a difficult quest. WPOs take many different organisational forms depending on the complexity of local realities, ranging from advanced collective organization as cooperatives to small self-help groups and microentrepreneurs. Self-organisation into regional and national networks provides economic opportunities, autonomy and stability as well as political influence. Yet, institutional support is fundamental and the lack thereof threatens their existence. Sustaining WPOs as important providers of socio-environmental benefits through governmental and non-governmental actions is a worthwhile undertaking that builds sustainability.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".