A Starting Point for Building Awareness for Indigenous Ways of Knowing in Engineering Leadership, Sustainability, and Design
Bibliographic record
Abstract
The authors of this paper are non-Indigenous educators who desire to introduce Indigenous Ways of Knowing into the engineering curriculum because they believe it can add value to the impact of engineering in society and positively influence approaches to sustainable engineering design. This is challenging as the authors do not have lived Indigenous experience nor deep foundational knowledge. This paper is a documentation and reflection on the authors’ learning journey and experiences in implementing Indigenous Ways of Knowing in learning resources and activities relating to engineering leadership, sustainability and design. The approach starts with the “work before the work” and describes various ways that Indigenous Knowledge was recognized and acknowledged. Outputs included an online learning resource, and virtual experiential learning workshops. The learning journey has increased our understanding of, and our ability to improve, the positive impact that Indigenous Ways of Knowing can have in the engineering curriculum.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.019 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.014 |
| Research integrity | 0.003 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".