A collaborative typology of boreal Indigenous landscapes
Bibliographic record
Abstract
Climate change and natural resource extraction are transforming boreal forest landscapes, with effects on Indigenous people’s relationship with the land. Collaborative management could enhance the consideration of Indigenous perspectives and limit negative outcomes of environmental change, but it remains the exception rather than the norm. We addressed barriers to involvement of Indigenous people in land management by developing a method to enhance communication and trust, while favouring bottom-up decision-making. We partnered with the Abitibiwinni and Ouje-Bougoumou First Nations (boreal Quebec, Canada) (i) to develop indicators of Indigenous landscape state, (ii) to create a typology of Indigenous hunting grounds, and (iii) to suggest guidelines for sustainable land management in Indigenous contexts. Through participatory mapping and semidirected interviews with 23 local experts, we identified factors influencing Indigenous landscape value. Using open-access data, we developed indicators to measure landscape state according to those values. We identified four types of hunting grounds with k-means clustering, based upon biophysical factors and disturbance history. Our results suggest that land management should aim to reduce differences between hunting ground states and consider the risk of rapid shifts from one state to another.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.000 |
| 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".