Weaving Knowledge Systems In Wildlife And Ecosystem Health
Bibliographic record
Abstract
In the last decade, post-secondary institutions and academics have been called upon to advance their understanding of reconciliation and to mainstream reconciliation in all aspects of the scientific endeavor. Wildlife and ecosystem health is a shared concern between Indigenous and non-Indigenous Canadians and weaving Indigenous and Western-based ways of knowing can provide a holistic approach and understanding to these problems. To date there is no known study that reviews the literature on weaving ways of knowing in wildlife and ecosystem health. We conducted a systematic review of the peer-reviewed and grey literature (6,991 publications) – screening for and including studies that weave Indigenous and Western-based ways of knowing to study wildlife health and environmental contaminants in the Canadian context. We coded information from several categories including publication timing and frequency, study locations, research partners and Indigenous knowledge holder information, wildlife health stressors, ecological scale and research subject, methods and methodologies, Indigenous participation across research stages, and outcomes and results sharing to assess trends related to knowledge weaving. We found 17 studies that satisfied our inclusion criteria, most of which took place in Canada’s north (Yukon, Northwest Territories, Inuvialuit Settlement Region, Nunavut, and Nunavik). Research partnerships most often occurred between First Nation or Inuit knowledge holders and Western-based academics. The health stressors metals (n=8) and avian cholera (n=2), and the species lake trout, lake whitefish, arctic char, caribou, muskoxen, and common eider (n=2) were studied most often. The methodology used to weave ways of knowing was most often community-based participatory research coupled with interviews, tissue sampling, and field data collection. We additionally analyzed two exemplar case studies through a decolonial lens to provide a more in-depth understanding of the process of conducting collaborative research with Indigenous communities. We concluded that research that weaves ways of knowing must not be approached with a ‘one-size-fits-all' mindset but instead should emphasize relationship building, continuous engagement, and ethical practices. Overall, our review highlights potential approaches to conducting collaborative research weaving Indigenous and Western-based ways of knowing and offers insight in how research can respond meaningfully to calls for reconciliation in Canada.
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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 | 0.055 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.027 | 0.034 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".