Waste Picker Organizations and Their Contribution to the Circular Economy: Two Case Studies from a Global South Perspective
Bibliographic record
Abstract
The discussion on the circular economy (CE) has attracted a rising interest within global policy and business as a way of increasing the sustainability of production and consumption. Yet the literature mostly portrays a Global North perspective. There is a diverse spectrum of community-based organizations playing important roles in resource recovery and transformation, particularly, but not only, in Global South countries, providing innovative examples for grassroots involvement in waste management and in the CE. This article proposes to add a Southern lens, situated in the context of waste picker organizations, to the concept of CE. The discursive framework in this article couples ecological economy (EE) with social/solidarity economy (SSE), focusing not only on environmental sustainability but also on social, economic, political and cultural dimensions involved in production, consumption and discard. We acknowledge that grassroots movements contribute to policy making and improve urban waste management systems. The paper outlines two empirical studies (Argentina, Brazil) that illustrate how waste picker organizations perform selective waste collection services, engage with municipalities and industries, and practice the CE. The research reveals that social and political facets need to be added to the debate about the CE, linking environmental management and policy with community development and recognizing waste pickers as protagonists in the CE. Our findings emphasize a need for a change of persisting inequalities in public policy by recognizing the importance of popular waste management praxis and knowledge, ultimately redefining the CE.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".