A tale of two fire systems: indigenous fire stewardship in British Columbia and California
Bibliographic record
Abstract
Abstract Background An increasing wildfire problem in western North America has created a policy space for Indigenous fire stewardship (IFS) to mitigate wildfire. We compare how British Columbia and California have supported IFS—two jurisdictions with distinct ecosystems but similar histories of colonialism and its socio-ecological consequences. We examine how IFS is incorporated into each jurisdiction’s institutional framework, and the barriers to, and opportunities for implementation. Results Each jurisdiction’s approach to recognizing IFS is shaped by different constitutional frameworks and legal relationships with Indigenous Peoples. California recently developed policies and planning documents to support IFS and enable co-stewardship and contracting agreements similar to the policies of some federal agencies. However, barriers related to land tenure constrain IFS practitioners and inhibit meaningful implementation across broader landscapes. Compared to California, British Columbia has not shown as much openness to supporting independent IFS practitioners, but instead has begun a project to integrate aspects of IFS into the existing provincial wildfire service. While British Columbia has expressed interest in working toward a shared decision-making approach with First Nations, the present framework restricts IFS to Indigenous land tenures (which comprises only 0.4% of the province). Conclusions Despite legal and policy changes to support IFS since 2017, deep-seated constraints prevent systematic implementation at a meaningful scale in both jurisdictions. Laws cannot by themselves catalyze social change; they must be complemented by a suite of initiatives to transform the social context. Some of these changes to enable IFS include government support for Land Back and land access for IFS practitioners; the removal of agency silos; building awareness of, and support for IFS within agencies and among the public; and providing resources for Indigenous Peoples to steward landscapes throughout the year, to achieve multiple goals.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".