The right to burn: barriers and opportunities for Indigenous-led fire stewardship in Canada
Bibliographic record
Abstract
Indigenous fire stewardship enhances ecosystem diversity, assists with the management of complex resources, and reduces wildfire risk by lessening fuel loads. Although Indigenous Peoples have maintained fire stewardship practices for millennia and continue to be keepers of fire knowledge, significant barriers exist for re-engaging in cultural burning. Indigenous communities in Canada have unique vulnerabilities to large and high-intensity wildfires as they are predominately located in remote, forested regions and lack financial support at federal and provincial levels to mitigate wildfire risk. Therefore, it is critical to uphold Indigenous expertise in leading effective and socially just fire stewardship. In this perspective, we demonstrate the benefits of cultural burning and identify five key barriers to advancing Indigenous fire stewardship in Canada. We also provide calls to action to assist with reducing preconceptions and misinformation and focus on creating space and respect for different knowledges and experiences. Despite growing concerns over wildfire risk and agency-stated intentions to establish Indigenous Peoples as partners in wildfire management, power imbalances still exist. The future and coexistence with fire in Canada needs to be a shared responsibility and led by Indigenous Peoples within their territories.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.006 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| 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".