Indigenous Guardianship and Moose Monitoring: Weaving Indigenous and Western Ways of Knowing
Bibliographic record
Abstract
Increasing global rates of wildlife species extinctions, extirpations, and declines warrant improvements to population monitoring and management approaches. To address regional environmental and wildlife issues, Indigenous communities globally are re-establishing traditional roles as stewards of the land through emerging Indigenous Guardianship Programs (IGPs). By providing the opportunity for community-level participation in monitoring and management, IGPs help foster cohesive solutions for long-term conservation of species while promoting environmental stewardship at the community level. Addressing challenges in monitoring and management of wildlife is especially critical for species that are of cultural and ecological importance at both community and distribution-wide scales. Herein, we describe IGPs in Canada with a focus on moose (Alces alces), an important species to many Indigenous Peoples across the species’ distribution. We outline common Western approaches to moose monitoring applied across Canadian jurisdictions and discuss ways in which weaving Indigenous knowledge systems and information gathered through local participation from Indigenous communities enhances monitoring initiatives at regional levels. We elaborate on a case study on moose monitoring and co-management in the community of Gitanyow in British Columbia, Canada to highlight the value of Guardianship to communities and species conservation in relation to moose. Our study reveals how IGPs and the weaving of Indigenous and Western knowledge systems can contribute to the maintenance of both ecological and cultural integrity to strengthen wildlife monitoring and management under changing global environments.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".