Talking about fire: Pikangikum First Nation elders guiding fire management
Bibliographic record
Abstract
In this paper, we present how elders of Pikangikum First Nation in northwestern Ontario have drawn upon their knowledge and values associated with fire to engage in fire management planning for 1.3 million hectares of their traditional boreal forest territory. Over a period of 18 months, we engaged in collaborative research strategies that included interviews, visits to historic fire sites, and community meetings with Ontario Ministry of Natural Resources (OMNR) to document the elders’ understandings of fire behaviour, forest disturbance and renewal cycles, traditional controlled burning practices, and perspectives on current fire management policies. The elders demonstrated the relevance of their knowledge of fire to contemporary planning efforts affecting woodland caribou habitat and fire management at site and landscape scales within their territory. We identified three themes and six recommendations that elders confirmed as priorities for future fire management planning. The three themes include (i) the need for continuing dialogue for fire management planning with OMNR, (ii) extending traditional teachings of fire safety to community youth, and (iii) the desire to re-engage in fire management using traditional processes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".