Bridging Indigenous knowledge systems and Western science for the co-management of wildlife in Canada: a systematic review
Bibliographic record
Abstract
Drawing upon both Indigenous Knowledge Systems (IKS) and Western science (WS) enhances equity and effectiveness, while also strengthening the evidence-base for wildlife management, decision-making, and conservation action. However, implementing this approach remains challenging. We systematically reviewed the published literature that reported empirical results on bridging IKS and WS in decision-making processes and the implementation of practices designed to influence interactions between people, wildlife, and habitats. The aim was to examine the relationship between wildlife management, co-management, and knowledge bridging in Canada. Our standardized search across bibliographic databases identified 21 articles, including 27 case studies for analysis. In addition to bibliographic and contextual information (e.g., location), we coded for 11 functional questions across categories: (1) decision-making; (2) implementation of practices; (3) achievement of impacts; and (4) enablers and barriers to success. Through our analysis, we make the case that effective knowledge bridging requires active participation of Indigenous Peoples throughout the wildlife management process, including setting objectives, selecting/designing, and implementing actions, followed by monitoring and evaluating performance. Our findings highlight essential enablers (e.g., cross-cultural competency, trust and relationship building, knowledge co-production) that need to be fostered alongside the barriers to overcome (e.g., epistemological/ontological divides, poor power sharing arrangements, communication difficulties) for equitable and effective mobilization of knowledge bridging in wildlife management.
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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 | 0.017 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.026 | 0.037 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".