Participatory Visual Methods and the Mobilization of Community Knowledge: Working Towards Community-Derived Disaster Risk Management in the Context of Advancing Climate Change
Bibliographic record
Abstract
Understanding climate-related challenges and generating effective interventions against them often lacks the knowledge of those who directly experience those challenges as a lived reality. We use the ‘Water and Fire’ project as an example of a research process undertaken to mobilize community knowledge on environmental disaster risk management. We worked with site-specific groups of community-based co-researchers who live in three marginalized areas of Cape Town that are susceptible to fire outbreaks, flooding, and water scarcity. We took a layered participatory visual methods approach, including digital storytelling, community mapping and photovoice, to differentially demonstrate how these hazards are experienced at household, neighbourhood, and community levels from the perspective of community-based co-researchers. The on-going enquiry enabled the co-researchers to illustrate and describe the biggest challenges they face and propose what they see as the most practical and promising solutions. We then facilitated a process of participatory analysis, triangulation of data and democratic decision-making amongst the co-researchers, through which they formulated a series of community-derived ‘Best Bets’ to better manage disaster risks. As part of the analysis, the co-researchers selected the stories, maps and photographs they wanted to present at stakeholder engagement events in making a case for the relevance and urgency of their Best Bets. We conclude that the participatory visual methods approach taken in the ‘Water and Fire’ project can be used as a model to strengthen the mobilization of local knowledge and further possibilities for community-derived disaster risk management in the context of advancing climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.407 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".