Contribution of waste pickers' grassroots organizations to waste management in Victoria and Vancouver
Bibliographic record
Abstract
The transition to a circular economy, recovering as much waste as possible and avoiding sending it to incinerators and landfills, presents several challenges, such as the lack of source separation and the mixing of waste that occurs at events and in public bins, for example. In this scenario, important actors are already doing the work of collecting and recovering recyclables, the so-called waste pickers (or binners), but they are not yet properly recognized. Despite their importance, their presence in countries, such as Canada, is still underexplored in the literature. Seeking to cover this gap and to explore how these actors can contribute to waste management and circularity, this chapter will present two case studies of grassroots organizations that promote waste management services provided by waste pickers in British Columbia: the Binners’ Project in Vancouver and the Diverters Foundation in Victoria. We will present a contextualization of waste legislations in the province and the cities studied, as well as information about the two initiatives, showing how their work contributes to waste management. The study is embedded within the urban political ecology framework.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.015 | 0.005 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".