Rethinking natural hazards research and engagement to include co-creation with Indigenous communities
Bibliographic record
Abstract
Internationally there is no single agreed definition of Indigenous peoples, and here we use Indigenous as an all-encompassing international term (in Canada this includes First Nations, Métis and Inuit). The United Nations Office for Disaster Risk Reduction (UNDRR) reports 1 that 476 million people in more than 90 countries identify as Indigenous and ~20% of the Earth is covered by Indigenous territories. Consequently, Indigenous peoples globally live at risk from natural hazards (e.g., volcanic eruptions, landslides, earthquakes) and also receive benefits from living in active geological areas (e.g., fertile soils, tourism, geothermal power). According to 2021 Canadian census data 2 , in the Province of British Columbia (B.C.) there are 290,210 people who identify as Indigenous, with 180,085, 97,865 and 1725 people self-identifying as First Nations, Métis and Inuit, respectively. All these people are susceptible to natural hazards and, as illustrated by Fig. 1 , the largest volcanic eruptions, earthquakes, landslides, wildfires, and floods in British Columbia, Canada have all affected Indigenous territories. Thus, given the global spatial overlap between Indigenous peoples and natural hazards, and the need for meaningful collaboration, both research and Indigenous local knowledge should be shared for mutual benefit. Here, we outline how the Nisg̱a’a First Nation and volcanology researchers have initiated and maintained a fruitful collaboration with bi-lateral knowledge exchange and resource co-creation. Furthermore, this comment article is co-written by non-Indigenous volcanology researchers based at universities (Jones and Williams-Jones) and a government organisation (Le Moigne) and by Indigenous scholars of the Nisg̱a’a First Nation, based at a post-secondary education establishment (Nyce and Nyce Jr.).
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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 | 0.084 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.030 | 0.062 |
| Scholarly communication | 0.023 | 0.037 |
| Open science | 0.006 | 0.061 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".