The natural hazard sector's engagement with Indigenous peoples: a critical review of CANZUS countries
Bibliographic record
Abstract
Abstract Natural hazard management agencies across the settler countries Canada, Australia, Aotearoa New Zealand, and the United States (or CANZUS countries) are presently involved in an increasing range of collaborative and consultative engagements with Indigenous peoples. However, perhaps because these engagements are diverse and relatively recent, little has been written about how they emerged and, from these agencies' perspectives, little is known about how these engagements find their motivation within government natural hazard management frameworks. In this article, we review existing academic and grey literature to categorise the origins of recent and present engagements and then identify and elaborate on the key rationales informing natural hazard management agencies' interactions with Indigenous peoples. We argue both that the broad principles of sustainability and inclusion have transformed these interactions and that developmentalist approaches and an overemphasis on Indigenous peoples' traditional knowledge can sometimes undermine this work. Incorporating critiques of settler colonialism relevant to the CANZUS context, this review aims to support established, emerging, and future collaborative engagements by investigating and analysing the literature to date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".