Climate Change-Related Natural Hazards and Risk Communication: Incorporating Traditional Indigenous Knowledge
Bibliographic record
Abstract
This chapter explores the incorporation of traditional Indigenous knowledge into climate change-related natural hazard risk communication. Ample research has been conducted on climate change-related risk communication, and a significant body of literature exists on the role of traditional Indigenous knowledge in reducing climate change impacts. However, even in the face of mounting climate change-related risks, little effort has been made to incorporate traditional Indigenous knowledge into climate change-related natural hazard risk communication. Scientific knowledge and traditional Indigenous knowledge pertain to different knowledge systems; however, in terms of methods and content, many aspects exist where both systems converge or follow similar patterns. Rather than focusing on points of divergence, researchers, policymakers, and decision-makers, and risk-communication experts should focus on common features of both systems. Points of convergence may provide common ground for knowledge integration and co-production, enabling Indigenous and scientific understandings of climate change to be reconciled. This may help improve risk communication processes between disaster risk management practitioners, agencies, and Indigenous Peoples. However, it is also important to recognize that traditional Indigenous knowledge may not fit with every scientific model; therefore, a more in-depth research is needed to learn which forms of traditional Indigenous knowledge can help scientific researchers improve climate change-related natural hazard risk communication processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".