Educação Ambiental e Mobilização com catadores de materiais reciclá-veis: solução de problemas, resgate de autoestima e construção de autonomia
Bibliographic record
Abstract
The study is part of the doctoral thesis and analyzes pickers of recyclable materials in the metropolitan region of São Paulo's training work. The goal is to identify elements to support participatory construction of sustainable solutions for social and environmental problems of solid waste in urban areas. These educational activities are in the context of dialogues on environmental education, focused on the implementation of public and institutional policies through participatory management of solid waste. Today, Brazil is witnessing the implementation of new processes of Integrated Waste Management, which guidelines include the participation of collectors and therefore there is need to expand the selective collection and professionalism in cooperative work, in this text, there are outstanding contributions to the collectors, with the completion of the Focus Group technique in 2008, the Participatory Management Project and Sustainable Waste - from 2005 to 2012, under an agreement between FEUSP, FAFE and UVic, and financial support of the Canadian International Development Agency. Meetings were held focused on trainers and collectors. The main contributions were on personal development, self-appreciation, the internalization of cooperative values and solidarity economy, and also the analysis of the socio-political and environmental contexte of life of these workers.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".