Collaborative everyday adaptation to deal with peatland fires: a case study on the east coast of Sumatra, Indonesia
Bibliographic record
Abstract
Actors across multiple levels, such as the private sector, national and subnational government institutions, and local communities, are expected to have the capacity to adapt to climate impacts and risks. This study analyzes how collaborative governance has been developed and carried out by multiple actors in everyday life to adapt to peatland fires in a situation where climate change variability drives fire occurrences. The case study research was undertaken on the east coast of Sumatra, Indonesia, where the challenge of annual peatland fires has increased in the last 15 years. The qualitative data were collected through participatory observations, face-to-face interviews with 35 key informants, and document analysis conducted in 2020. The research finding shows that structural arrangements, knowledge and learning, and resource sharing are essential dimensions in generating collaborative governance to adapt to peatland fires. Multiple actors in the community case study applied collaborative activities during the three adaptation stages: (1) anticipatory measures, (2) preparedness, and (3) responses through constructing canal blocks, conducting fire patrols, and fighting fires. Those collaborative activities are performed in everyday life and have reduced the potential occurrence of fires and the vulnerability of villagers to peatland fires. The study also highlights the effects of domination when powerful actors are unwilling to collaborate meaningfully with local actors, who sometimes share different interests and hierarchical positions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".