Using a community-driven approach to identify local forest and climate change priorities in Teslin, Yukon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The likelihood of addressing the complex environmental, economic, and social/cultural issues associated with local climate change impacts is enhanced when collaborative partnerships with local people are established. Using a community-centered approach in the Teslin region of Canada’s Yukon Territory, we utilized our research skills to respond to local needs for information by facilitating both an internal community process to clarify traditional and local knowledge, values, and perceptions on locally identified priorities, while gathering external information to enable local people to make sound decisions. Specifically, we sought to clarify local perceptions surrounding climate change impacts on fire risk and wildlife habitat, and the potential adaptation strategies appropriate and feasible within the Teslin Tlingit Traditional Territory. This paper provides a characterization of the study region and our project team; provides background on the interview and data collection process; presents our key results; and discusses the importance of our findings and charts a way forward for our continued work with the people in the Teslin region. This approach presents an excellent opportunity to help people holistically connect a range of local values, including fire risk mitigation, habitat enhancement, economic development, and enhanced social health.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it