Weaving ways of knowing in practice: a collaborative approach to prioritizing community knowledge and values in wildlife camera monitoring with Magnetawan First Nation
Bibliographic record
Abstract
There is not one singular way to weave together Indigenous and Western knowledges; creating meaningful cross-cultural collaborations requires a foundation of relationships rooted in the context of specific people and place. As interest in working across knowledge systems increases, our goal is to provide an example of respectful and appropriate cross-cultural collaboration within environmental practice. We demonstrate our collaborative, mixed-methods approach to developing a community-based wildlife monitoring program with Magnetawan First Nation that prioritizes community knowledge and values. Through community interviews and a youth sharing circle, participants highlighted values (respect, interconnection, reciprocity, collaboration, and relationship) as well as research priorities, providing examples of what each may look like in practice, to inform our monitoring approach. These examples, paired with reflections from the research team, are shared to explore the process of weaving together knowledge and values to co-create a community-based wildlife monitoring program, applying wildlife cameras as much more than simply a tool for data collection. This research provides tangible examples of weaving together knowledges and values in the context of environmental monitoring, helping guide future cross-cultural collaboration to ensure this work is being done in a good way.
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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 | 0.039 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.031 | 0.023 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.004 | 0.005 |
| 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".