Weaving Indigenous and Western Science Knowledges Through a Land-Based Field Course at Bkejwanong Territory (Laurentian Great Lakes)
Bibliographic record
Abstract
In response to a growing interest in building Indigenous-led educational experiences, we codeveloped a land-based field course that wove Indigenous ways of knowing together with Western ecological concepts. The spirit of the course was the one rooted in varied ways of knowing nature, on the land, the water, and the culture—to see the Great Lakes from an Anishinaabe perspective. Situated in the heart of the Laurentian Great Lakes Basin at Bkejwanong Territory (Walpole Island First Nation), in the Traditional Territory of the Three Fires Confederacy of First Nations (Ojibwe, Odawa, and Potawatomi) on Turtle Island (North America), this inaugural undergraduate university course was led by an Indigenous instructor with contributions from non-Indigenous science faculty from the university and local community knowledge keepers. Here, we describe our journey in cocreating land-based teaching modules with Indigenous scholars and scholars at the University of Windsor, Ontario, Canada. We focused on experiences that exposed students to traditional ways of knowing nature, and reflections were used as the main teaching pedagogy. The course offered daily perspectives and activities across land and water and examined dimensions of biodiversity as sacred beings and medicine. Outcomes and indicators of success were driven by the individual’s reflection and evaluation on their own growth, as expressed through a final project aimed at bridging knowledges, supporting community initiatives or both. This case is designed to offer an example that has potential for application to many other contexts where community-faculty partnerships and land-based learning opportunities are available.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".