Rebuilding Yunesit’in fire (<i>Qwen</i>) stewardship: Learnings from the land
Bibliographic record
Abstract
Yunesit’in First Nation is reclaiming fire stewardship after generations of suppression. Applying a “learning by doing” approach, Yunesit’in members plan and implement proactive fire practices to the landscape, which are low intensity cool burn fires driven by the needs of the landscape and community goals. Through a structured monitoring and evaluation process, the participants generate knowledge and science on fire stewardship; the outcomes are documented and mobilized in various ways, including video, photos, and peer-reviewed articles. The pilot program has initially been evaluated through four general measures: area stewarded (in hectares); people employed and trained (number and diversity of people employed); the level of planning, vision, and program sustainability (generating plans where fire is a tool to meet the goals in these plans, supported by carbon funds); and partnerships and knowledge mobilization, (fostering partnerships for knowledge production and mobilization). On these measures, the program is growing and is a success. A holistic framework is being developed by the community, which encompasses ecological, social, economic, and cultural indicators, including a health and wellbeing evaluation framework to assess the physical, mental health and wellbeing benefits for participants in the program. A holistic approach is critical for understanding the connection between people, place and the role that fire stewardship plays in mediating positive outcomes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".