Indigenous Knowledge in Introductory Chemistry: Use of an Interior Salish Pit-Cooking Practice as a Rich Contextual Framework
Bibliographic record
Abstract
In response to the Calls to Action of Canada’s Truth and Reconciliation Commission, a learning activity has been developed for use in introductory chemistry, exploring a traditional food-preparation practice of the Syilx and Secwépemc Peoples of British Columbia’s Interior Plateau. The activity begins the course with framing questions highlighting the sophisticated Indigenous knowledge involved in an elaborate and precisely engineered pit-cooking process. As the course progresses, the activity revisits and answers these questions using chemical principles of kinetics, thermodynamics, acid–base chemistry, and organic substitution reaction mechanisms. In alignment with principles of Two-Eyed Seeing, the learning activity fosters appreciation for the sophistication of regional Indigenous knowledge, presented as complementary to Western science. Survey responses ( N = 207) indicate a high student interest and engagement with the topic, and thematic analysis of open-ended written responses regarding the activity ( N = 33) reveal recognition of a meaningful application of multiple course concepts, admiration for the depth and complexity of the Indigenous knowledge involved, and appreciation for the authentic manner in which an Indigenous cultural context was integrated into the curriculum, demonstrating the activity’s success in promoting an understanding and respect for Indigenous knowledge within a scientific framework.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".