Experiences with integrative Indigenous and Western knowledge in water research and management: a systematic realist review of literature from Canada, Australia, New Zealand, and the United States
Bibliographic record
Abstract
The implementation of Indigenous and Western knowledge systems in integrative water research and management is gaining prominence in the realm of academia, particularly in four countries with a shared, albeit different, history of British colonialism: Canada, Australia, New Zealand, and the United States. While integrative water research in particular is gaining popularity, currently there is a gap in our understanding regarding where, when, why, how, and for whom this type of research has been successful. A systematic review method was used to identify peer-reviewed literature from each of the four countries and to understand where and when integrative water research projects were taking place. Then, we used a realist review method to synthesize and analyze the included peer-reviewed literature to determine why, how, and for whom this type of research has been successful, or not. Our systematic literature search provided 669 peer-reviewed articles from across the four countries, of which 97 met our inclusion criteria and were analyzed. Our findings indicate that the total number of integrative water research projects has been increasing since 2009, though these projects are largely concentrated within the realm of social science and conducted by non-Indigenous authors. Recognition of the value of Indigenous knowledge systems, coupled with an understanding that the use of Western knowledge systems alone has not remedied the disparity in access to safe water sources in Indigenous communities, has led researchers to recommend collaborative partnerships and governance structures as a potential pathway to effective integrative water research. Our research was conducted to enhance contemporary understanding of the strengths of implementing Indigenous and Western knowledge systems and to encourage readers to continue working towards a common goal of reconciliation and equality in all partnerships.
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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 | 0.086 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.015 | 0.023 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| 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".