Integrating Indigenous and scientific perspectives on environmental changes: Insights from boreal landscapes
Bibliographic record
Abstract
Abstract Major environmental changes affect the health and capacity of ecosystems to sustain Indigenous people's well‐being in boreal landscapes. Collaboration between Indigenous communities and researchers could help assessing and mitigating the consequences of environmental changes. We used Driver Pressure State Impact (DPSI) conceptual models to compare the perspectives of Indigenous and scientific communities on environmental changes in boreal landscapes of Quebec, Canada. The Indigenous DPSI model emerged from interviews with local land‐use experts from two Indigenous communities. The scientific model was informed by the publication topics of expert researchers. We compared the Indigenous and scientific models and exposed convergences and divergences between perspectives. Forestry was identified as a major driver of change in both models. Most issues related to mining, hydro‐power and forest road development were specific to the Indigenous model. Climate change and wildfires were of greater interest in the scientific model. Convergences between the perspectives of Indigenous and scientific communities are conducive to collaborative research. Divergences could be addressed through reciprocal knowledge transfer activities, which would lead to research that better aligns with the concerns and needs of Indigenous communities. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".