Indigenous Knowledges of forest and biodiversity management: how the <i>watchfulness</i> of Māori complements and contributes to disaster risk reduction
Bibliographic record
Abstract
The United Nations Sendai Framework 2015-30 for disaster risk reduction (DRR) reaffirms the role of Indigenous Knowledges (IK) as complementing and contributing to more effective DRR. This hard won space for IK comes as Indigenous communities voluntarily contribute to the local management of disasters, including wildfire and threats to biodiversity in forest ecosystems. The effectiveness of Indigenous practices in addressing hazards is based on traditional knowledges and empirical observations that inform active roles in environmental management. However, it is still not clear how IK complements and contributes to DRR. This article analyses interviews with elders, researchers, and community members and identifies how mātauranga Māori (Māori knowledge) on forests and biodiversity is embodied to inform Indigenous watchfulness as a tactical approach in contributing to more effective DRR strategies.
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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 | 0.000 | 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".