From Science to Sovereignty: Indigenizing Western Scientific Approaches for Culturally Appropriate Wildfire Recovery
Bibliographic record
Abstract
ABSTRACT As catastrophic wildfires increasingly devastate the interior landscapes of British Columbia, Canada, conventional approaches to post‐wildfire recovery often overlook Indigenous values, knowledge systems, and food sovereignty. In collaboration with six Northern St'át'imc communities and guided by the “walking on two legs” framework, which brings together Indigenous and Western knowledge systems led by Indigenous worldview, we reanalyzed post‐wildfire vegetation trajectory data from the McKay Creek wildfire. We replaced colonial‐era “native/non‐native” plant classifications with culturally grounded categories to better reflect Indigenous wildfire recovery priorities. Vegetation trajectories were based on percent cover data collected across 80 plots, stratified by burn severity and pre‐wildfire invasive plant presence. Our results show that conventional plant classifications may obscure critical vulnerabilities in Indigenous traditional plant food systems and protein sources. While “native” plant cover exceeded 25% across all treatments, our cultural plant classifications ranged from just 7% to 18%. While elevation emerged as a key factor in post‐fire vegetation dynamics, government‐defined “Mule deer forage” plant classification indicated increased forage at higher elevations, while the Indigenous‐informed classification, “Mule deer preferred forage” showed the opposite trend. These findings demonstrate that classifying plants by cultural knowledges reveals a more accurate picture of post‐wildfire recovery. Conventional plant classifications may overestimate ecosystem resilience and thus overlook areas requiring urgent intervention. Indigenizing Western scientific approaches can strengthen ecological restoration by aligning data interpretation with Indigenous sovereignty and community‐led priorities. This study offers a concrete model for advancing culturally appropriate wildfire recovery while supporting the implementation of legal obligations to protect Indigenous food sovereignty under Section 35 of the Constitution Act, 1982, and British Columbia's Declaration on the Rights of Indigenous Peoples Act (DRIPA). Our study suggests that reframing plant classification by Indigenous values not only deepens understanding of post‐wildfire recovery but also supports more effective, place‐based decision‐making for long‐term ecosystem stewardship.
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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 | 0.034 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.001 | 0.005 |
| 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".