Classification struggles of informal workers: How waste pickers became proletarianized in Brazil and Indigenized in Colombia
Bibliographic record
Abstract
Why do movements of similarly situated workers arrive at different answers to fundamental questions like: Who are we? What do we want? How will we get it? And how do answers to these questions shape movement strategy and outcomes? This article explores this puzzle by comparing the Brazilian and Colombian waste picker movements—two of the world’s oldest and largest organizing efforts among informal recyclers. Despite shared origins, the movements diverged sharply in their discourses, strategies, and demands. In Brazil, waste pickers aligned with the Workers’ Party and adopted a class-based discourse, portraying themselves as exploited laborers fighting for economic inclusion. In Colombia, by contrast, the movement lacked allies in elected office and pursued a human rights strategy, framing waste pickers as a vulnerable population facing displacement by the state. Drawing on 26 months of ethnographic research, the article traces how these divergent self-classifications shaped legal frameworks, public policies, and material outcomes in each country. To explain these differences, it draws on two concepts from Bourdieu’s sociology—classification struggles and political fields—which illuminate how informal worker movements innovate, and why such innovation takes distinct forms across political contexts. The comparison shows that reimagining informal work as “decent work” is not a neutral process, but a contested terrain of meaning-making and institutional struggle. It underscores the importance of centering workers’ own definitions of dignity, autonomy, and fairness.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".