Incorporating Indigenous Knowledge Systems into Collaborative Governance for Water: Challenges and Opportunities
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it