Deliberation for wildfire risk management: Addressing conflicting views in the Chiquitania, Bolivia
Bibliographic record
Abstract
Wildfires are increasingly affecting forest landscapes around the world. In the Bolivian Chiquitania, southern Amazonia, large wildfires during recent droughts have intensified public debate around more systemic solutions to address the possible root causes. While the integration of different forms of fire knowledge is gaining acceptance as an approach to dealing with increasing wildfire risk, little attention has been given to this integration in the Amazonia. In fact, mismatches between policy, science and local realities have curtailed the success of fire risk strategies in the region. To address this challenge, we conducted interviews and focus group discussions with a wide range of actors in the Chiquitania to examine different forms of knowledge and views of fire, and the extent to which these were integrated in prevalent wildfire risk strategies. We found that the risk strategies were in tension between two conflicting understandings of fire. A conceptual framework was developed to capture the configuration of knowledge underpinning this tension. Adopting a more integrated and inclusive approach to manage wildfire risk will require overcoming first this tension through a more open deliberation process within a reflexive governance framework. We proposed three “deliberation arenas” to facilitate this process, which could ultimately support more systemic, inter‐cultural fire management in the Chiquitania and other landscapes with conflicting views in the Amazonia.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.018 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".