Community knowledge co-creation through participatory video
Bibliographic record
Abstract
This article describes the process and outcomes of a participatory video project with 22 catadore/as (‘ recyclers’) from recycling cooperatives in the metropolitan region of São Paulo, Brazil. During a week-long workshop (April 2008), leaders from participating cooperatives were trained in video technology, storyboard development, and postproduction media as a strategy to improve community–networking opportunities and to stimulate awareness and education of inclusive and integrated recycling programs. Through a participatory action research initiative, four short documentaries were then co-produced between 2009 and 2011 and a collaborative research design was developed to use the videos as a communication tool for enhancing dialogue with policy makers in three municipalities. This article explores the methodological and theoretical contributions of using participatory video as a strategy for mobilizing community knowledge. This research demonstrates the use of participatory video as a creative avenue to capture and nurture valuable knowledge often on the periphery, which can have powerful impacts when brought into centre stage. It also reviews theories of Community-based Participatory Action Research and Knowledge Democracy as central to expanding processes for participatory development and citizenship. The results reveal enhanced mobilization of this community and document the strengthening of partnerships between recycling cooperatives and municipal governments in the metropolitan region of São Paulo.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".