“To be involved in a meaningful way”: Mobilizing Indigenous Knowledge in Environmental Monitoring Practices in Northern Ontario
Bibliographic record
Abstract
A steady shift in the environmental management literature encourages greater inclusion of traditional knowledge (TK) alongside Western science, much of it seeking to directly support Indigenous communities develop their own frameworks for environmental monitoring and stewardship. To date, little attention has been placed on research practices themselves as sites where interdisciplinary and intercultural work takes place to bridge between different knowledge systems and develop best practices for effective collaboration. Matawa Water Futures (MWF), the object of study for this thesis project, is a three-year water stewardship project involving Indigenous and non-Indigenous researchers, environmental managers, and community interns, working with the nine member communities of Matawa First Nations in northern Ontario to establish a framework for water monitoring and stewardship based in Indigenous TK. Using ethnographic methods, this research addresses the shifts in ways of thinking necessary to bridge knowledge systems for environmental monitoring, the discursive practices mobilized around TK in relation to science, and the practical implications of these shifts in perception and discourse for efforts to establish Indigenous-informed approaches to environmental management. This research argues that the MWF project reflects a shift away from a hierarchical dynamic of power/knowledge towards a more horizontal space of interaction between Indigenous and Western knowledge, and to also assert Indigenous governance in relation to the environment.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".