USE OF AN INDIGENOUS LEARNING BUNDLE IN AN ENGINEERING PROJECT COURSE
Bibliographic record
Abstract
In response to the Calls to Action of the Truth and Reconciliation Commission of Canada a range of Collaborative Indigenous Learning Bundles have been introduced at a Canadian university to provide ways for Indigenous knowledge to be incorporated into courses across the university. One of the first courses in the engineering faculty to use the Indigenous Environment Relations Bundle was a third year project course for the BEng Electrical Engineering program. The use of the bundle, through the learning management system, was part of the lecture series and was used in a class discussion with an optional reflection. The objective was to provide complementary Indigenous knowledge to the discussion of the environmental impact of engineering. A first year introductory engineering course used the First Peoples: A Brief Overview. The response of the students in both courses was respectful and produced thoughtful discussions. Those that undertook the optional reflection produced insightful and often personal thoughts on a particular place. The use of the bundles shows that Indigenous matters and information from knowledge keepers can be integrated into engineering courses.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".