Case Study to Illustrate Two-eyed Seeing and the Richness of Indigenous Knowledge in Food Preservation
Bibliographic record
Abstract
This work was motivated by the need to co-create an activity that illustrates the richness of Etuaptmumk (two-eyed seeing) and the complimentary nature of different ways of knowing. A fourth-year technical elective course at a Canadian University Chemical Engineering program was selected. The materials were co-developed using lived experiences of the team recognizing the richness of Indigenous knowledge systems and its relationship with Western ways of engineering knowledge. The delivery method was based on participatory learning, specifically story sharing, non-verbal/kinesthetic, and learning maps/interconnection. The making of pemmican (from the Cree pimîhkân) was selected as food preservation example that ties together traditional knowledge with food engineering nutritional and physico-chemical properties and preservation to examine the “energy bar.” The making of pemmican provided a tangible, hands-on learning experience tied to course topics demonstrating the richness of Indigenous knowledge, interconnectedness of the topics, and ways of learning outside of the traditional engineering lecture delivery structure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".