Inclusive Waste Governance and Grassroots Innovations for Social, Environmental And Economic Change
Bibliographic record
Abstract
Participants of two research projects (Recycling Networks: Grassroots resilience tackling climate, environmental and poverty challenges (funded by the Swedish Research Council) and Mapping Waste Governance (funded by the Social Sciences and Humanities Research Council of Canada) collaborate in offering a critical inter- and transdisciplinary perspective on waste and waste actors (waste picker cooperatives, associations, community-based organizations, partnerships, networks and NGOs). The research is conducted in the following cities: Buenos Aires (Argentina), São Paulo (Brazil), Vancouver and Montreal (Canada), Kisumu (Kenya), Managua (Nicaragua) and Dar es Salaam (Tanzania). Together we examine the challenges that innovative grassroots initiatives and networks encounter in generating livelihoods to improve household waste collection and recycling, particularly in informal settlements of global South cities. We seek to map waste governance and successful waste management initiatives, arrangements and policies involving grassroots initiatives. In this report, we present a brief description of solid waste governance in the cities where we conducted fieldwork. We then illuminate some of our findings on grassroots innovations involving waste pickers or waste workers in these cities. Both research projects combine multi-case studies of waste picker groups and local government initiatives, apply qualitative research tools and participatory action research (e.g. photo voice, participant observation, workshops, surveys and interviews). We are interested in understanding processes, challenges and opportunities related to how these grassroots initiatives and networks operate to bring about socio-environmental and economic change? How they address challenges and what the assets are in everyday waste governance that can be explored to make waste governance more sustainable and thus more inclusive? Researchers involved in these two projects, key stakeholders from grassroots initiatives in these countries, representatives from some international waste picker networks and local and regional government officials from Kisumu, Kenya, met between 23rd and 29th of April 2018, in Kisumu to present and discuss the results of the first year of research activities, which are herewith documented.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.025 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".