Decolonizing and Indigenizing Engineering: The Design & Implementation of a New Course
Bibliographic record
Abstract
This practice paper introduces a new course designed by one Indigenous and one non-Indigenous engineering educator at the University of Manitoba to decolonize and Indigenize engineering. Working with an Indigenous teaching assistant, and supported by a doctoral student auditing the course, we facilitated a small group of Indigenous and non-Indigenous engineering students to think critically about making place and space for Indigenous Peoples and worldviews in engineering. Here, we share the course design, our reflections on the course, and our plans going forward. Our initiative is one answer the Calls to Action by the Truth and Reconciliation Commission (TRC) of Canada to learn the truth about Canada as colonizer and use education as a tool for reconciliation. In doing so, we aim to provide engineering students with knowledges and perspectives for working successfully with First Nations, Métis and Inuit Peoples and communities in engineering practice in Manitoba, and in Canada.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".