Traditional and local communities as key actors to identify climate-related disaster impacts: a citizen science approach in Southeast Brazilian coastal areas
Bibliographic record
Abstract
The impacts of climate-related disasters can be estimated by climate models. However, climate models are frequently downscaled to specific settings to facilitate Disaster Risk Management (DRM) to better understand local impacts and avoid overlooking uncertainties. Several studies have registered the increasing importance of recognizing traditional knowledge, co-design, and collaboration with local communities in developing DRM strategies. The objective of this research was co-design local-scale observations with traditional and local communities to characterize their local context regarding the impacts of climate-related disasters. The citizen science approach coupled with participatory action research was conducted with two traditional communities in the Southeast of the Brazilian coast: Quilombo do Campinho da Independência in Paraty, Rio de Janeiro, and the Caiçara (artisanal fishing) community of Ubatumirim in Ubatuba, São Paulo. Working groups were organized with leaders to become community researchers, conducting interviews and actively mobilizing their communities. A structured questionnaire was developed, adapting 22 variables taken from the Protocol for the Collection of Cross-Cultural Comparative Data on Local Indicators of Climate Change Impacts—LICCI Protocol. A total of 366 impacts were analyzed, after combining the georeferencing form data collected—Survey123 (280 impacts) and the interviews with community leaders (86 impacts). The results showed a significant level of cohesion (α = 0.01) between the Caiçara (artisanal fishers) and Quilombola (Afro-descendants) perceptions of climate-related events associated with their subsistence practices and climate variability. These findings highlighting the importance of DRM proposals that recognize traditional peoples and local communities as frontline vulnerable populations while acknowledging their role as key actors in identifying impacts, collecting data on land use and territory, subsistence-oriented activities, and cosmovision. However, it is still necessary to address climate change challenges at different scales. To do this, it is crucial to promote cognitive justice though the recognition of the values of the memories, perceptions and local knowledge, by scaling up locally-driven observations that empower local communities to lead their own climate adaptation efforts.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".