MétaCan
Menu
Back to cohort
Record W4409995277 · doi:10.4324/9781003534129-12

Contribution of waste pickers' grassroots organizations to waste management in Victoria and Vancouver

2025· book-chapter· en· W4409995277 on OpenAlexaboutno aff
Ana Maria Rodrigues Costa de Castro, Jutta Gutberlet

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicMunicipal Solid Waste Management
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsEnvironmental planningBusinessWaste managementGeographyPolitical scienceEngineeringLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.686
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.004
GPT teacher head0.207
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topicMunicipal Solid Waste ManagementFrench-language works237,207