Participatory solid waste governance and the role of social and solidarity economy: experiences from São Paulo, Brazil
Bibliographic record
Abstract
Waste governance is emerging as transdisciplinary and inter-sectoral approach to waste management and policy, overcoming primarily prescriptive engineering perspectives of waste. The process of governing waste involves the articulation of different structures, institutions, policies, practices and actors. Paying attention to issues of power, scale, and equity are important in the search for more democratic practices. Innovative forms of governance are emerging as decentralized, participatory and inclusive, focused on waste reduction and resource recovery. Social and Solidarity Economy (SSE) is an innovative alternative in generating work and income and a response in favor of social and labor inclusion. It can also be considered as a new, more humane and inclusive development model. With this article we aim to provide practical knowledge on the contributions of grassroots organizations and networks in waste management, supporting the discussion of waste governance in the context of the SSE. We present different experiences of waste picker organizations in the metropolitan region of São Paulo, Brazil to showcase their assets and to discuss prevailing challenges. Employing the SSE as a new development model allows us to address everyday issues of waste generation, management and governance in Brazilian cities and in other parts of the world; particularly from the perspective of organized waste pickers in associations, cooperatives and networks. This is a development paradigm which goes beyond just economic considerations, as highlighted with examples from waste management.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".