Canadian and Australian researchers' perspectives on promising practices for implementing Indigenous and Western knowledge systems in water research and management
Bibliographic record
Abstract
Abstract National and international policies have called for the inclusion of Indigenous peoples and the uptake of Indigenous knowledge alongside Western knowledge in natural resource management. Such policy decisions have led to a recent proliferation of research projects seeking to apply both Indigenous and Western knowledge in water research and management. While these policies require people with knowledge from both Western and Indigenous perspectives to collaborate and share knowledge, how best to create and foster these partnerships is less understood. To elicit this understanding, 17 semi-structured interviews were completed with academic researchers from Canada and Australia who conduct integrative water research. Participants, most of whom were non-Indigenous, were asked to expand on their experiences in conducting integrative water research projects, and findings were thematically analyzed. Our findings suggest that Indigenous and Western knowledge systems influence how one relates to water, and that partnerships require a recognition and acceptance of these differences. We learned that community-based participatory research approaches, and the associated tenets of fostering mutual trust and community ownership for such an approach, are integral to the meaningful engagement that is essential for developing collaborative partnerships to implement both Indigenous and Western knowledge systems and better care for water.
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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 | 0.093 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.050 | 0.029 |
| Scholarly communication | 0.019 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.005 | 0.007 |
| 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".