Centering Indigenous Voices: The Role of Fire in the Boreal Forest of North America
Bibliographic record
Abstract
Purpose of Review: Indigenous perspectives have often been overlooked in fire management in North America. With a focus on the boreal region of North America, this paper provides a review of the existing literature documenting Indigenous voices and the historical relationship of Indigenous peoples in northern North America to fire and landscapes that burn. Recent Findings: Early research on the topic explored how Indigenous people used fire in the boreal forest, with most research coming out of case studies in northern Alberta. Emerging research in the last two decades has broadened the geographic focus to include case studies in Alaska, Ontario, Labrador, and other regions in North America. This broadening of focus has shown that the diversity of Indigenous peoples in North America is reflected in a diversity of relationships to fire and landscapes that burn. Of note is an emerging interest in Indigenous fire knowledge in the wake of settler colonialism. Summary: Indigenous peoples in the boreal forest have applied fire on their landscapes to fulfill numerous objectives for thousands of years. More than a tool, Indigenous peoples in the boreal view fire as an agent, capable of movement, destruction and creation, acting on the landscape to create order, within a living, connected environment. Unfortunately, restrictions on the application of Indigenous fire knowledge and practice initiated during early colonial times remains a contemporary challenge as well. Supplementary Information: The online version contains supplementary material available at 10.1007/s40725-022-00168-9.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".