Cree knowledge, fuzzy cognitive maps, and the social-ecology of moose habitat quality under an adapted forestry regime
Bibliographic record
Abstract
Participatory modeling and fuzzy cognitive mapping of social-ecological systems offers a more comprehensive understanding of complex systems inclusive of multiple perspectives and diverse types of knowledge. Many Indigenous communities attribute recent declines in boreal moose populations to forestry disturbance and are insisting that their observations, knowledge, and values contribute more meaningfully to forestry and moose co-management. Here we describe a knowledge co-production approach documenting Cree social-ecological understanding of moose habitat quality in the Eeyou Istchee territory of northern Québec, Canada, almost 20 years after the implementation of a forestry co-management regime. Thirty-seven fuzzy cognitive mapping sessions with 56 land-users from 4 Cree communities identified 18 categories that influence good moose habitat, including physical (“Climate & Weather”), ecological (“Habitat Features, Moose Forage”), and social contributors (“Hunting & Predation, Cree Culture”). Knowledge maps highlight the diverse interrelationships that land users know to influence moose habitat quality and point to key social variables (hunting activity, noise disturbance) that should be included in wildlife-habitat models, as well as specific aspects of forestry practice and management that Cree know to negatively impact moose populations despite the implementation of a co-management regime. Our findings highlight how fuzzy cognitive mapping can bring together individual expertise into a collective knowledge account, inclusive of multiple understandings and experiences that allows for the identification and ranking of variables and relationships. Fuzzy cognitive mapping summarizes the plurality of Cree social-ecological knowledge in a form that is accessible, applicable, and actionable within local, regional, and provincial co-management regimes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".