Waste picker rights and social inclusion: the creation of a university with knowledge democracy
Bibliographic record
Abstract
UNICATA is a university for and with waste pickers based on Paulo Freire's popular education pedagogy, knowledge democracy and the practice of peer learning. The aim is to create a learning space of excellence where one can dream, dare, innovate, and be inspired by transformative ideas and achievements. This university will increase access to knowledge and expand the possibilities for reflection, for a population that suffers from social exclusion and high vulnerability. Worldwide waste pickers are major protagonists in collecting, separating, and redirecting recyclable materials into the circular economy. Research demonstrates that waste pickers are central figures in educating households on waste separation practices, adding value to recovered materials, building community by integrating socially excluded individuals into their collective workspaces, indirectly also mitigating environmental and climate impacts. While these positive effects of inclusive recycling are increasingly recognized in the academic literature, unfair remuneration, stigmatization, and risk-prone or unhealthy working conditions are still the prevailing realities. This paper discusses reflections on recent experiences of implementing UNICATA in the metropolitan region of São Paulo, Brazil, in 2023, with a pilot project developing and delivering the introductory module which was successfully completed by 22 students. The research takes a social constructivist lens to uncover the colonial social and political injustices through experiential and student-centered education. Our results reveal some noticeable assets and barriers in creating inclusive education for a large population that is widely neglected, in many different geographic contexts, thus also filling a gap towards achieving the Sustainable Development Goals (SDGs).
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.027 |
| Research integrity | 0.002 | 0.003 |
| 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".