Incorporating Indigenous Knowledge Systems into Collaborative Governance for Water: Challenges and Opportunities
Bibliographic record
Abstract
The importance of Indigenous knowledge systems for environmental decision-making is now widely recognized. In the context of collaborative approaches to environmental governance, scholars and practitioners have recognized that Western knowledge is not sufficient, and that ideas, practices, and knowledge from Indigenous peoples is essential. Collaborative environmental governance practice tends to make assumptions about how Indigenous knowledge systems can be incorporated into decision-making without reflecting satisfactorily on contrasting perspectives of Indigenous peoples themselves; these perspectives are partially captured in the Indigenous governance literature. This essay draws on empirical research in British Columbia, a place where First Nations have been approached by organizations involved in water governance to be involved in collaborative decision-making. The research reveals an important disconnect between the perspectives of Indigenous knowledge-holders and the people promoting “integration” of this knowledge into collaborative decision-making processes. We offer suggestions for reconciling collaborative approaches to water governance with Indigenous knowledge systems and the values and perspectives of Indigenous peoples.
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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 | 0.026 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.052 |
| Scholarly communication | 0.021 | 0.018 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.004 |
| 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".