A Scoping Study of Collaborative Wildfire Management: Association between Indigenous and Non-indigenous Peoples
Bibliographic record
Abstract
Facing the limitations of current wildfire management practices, increasing attention has been paid to indigenous wildfire practices that use fire for ground fuel reduction. This study investigated the cooperation and association between indigenous and non-indigenous peoples for current wildfire management, with a focus on prescribed burning, to provide a comprehensive review of activities. Through a scoping study, combined with media reviews and an in-depth analysis of collaborative initiatives, this study presents an overview of the research landscape, critical considerations, and research practices for conducting collaborative initiatives. Research shows a significant increase in the academic literature about integrating Indigenous Knowledge and practices into mainstream wildfire management in the last five years. The predominant countries in this research are Australia, the United States, and Canada, where colonial legacy still has a significant impact on indigenous peoples. Issues such as land ownership, resource allocation, and disruption of Indigenous Knowledge transfers continue to be present. This research emphasizes the importance of adopting decolonizing practices in project design and research practice to ensure mutual benefit from collaboration instead of using Indigenous Knowledge as a tool for disaster risk reduction objectives. Recommendations for further study include careful research and project design, explicitly providing the change mechanism and evaluation methods, providing context analysis, including health and environmental concerns, proactive research focus in less-studied regions, and detailed investigation of policies, donor relationships, and funding cycles that affect the environment to promote collaborative initiatives.
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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 | 0.047 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.001 |
| 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".