Decolonizing and Indigenizing Engineering: The Trauma of Learning the Truth about Canada’s Colonial Present & History in Curricula
Bibliographic record
Abstract
Engineering educators in Canada have a responsibility to seek the truth, commit to, and lead reconciliation efforts. This responsibility comes with other responsibilities: to be mindful of the impact that learning these truths have on Indigenous students and Indigenous faculty specifically, and to integrate content and supports to help students and faculty navigate the trauma triggered by these curricula. This SoTL paper discusses the second offering of a course designed to introduce engineering students to decolonizing and Indigenizing engineering. It shares the collective findings from the reflections on the experiences of several Indigenous and non-Indigenous students and one instructor who took the course. Based on our learnings from these reflections, recommendations are made for enhancing the course design to better prepare students for the traumatic impact of learning a decolonized and Indigenized 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.046 | 0.055 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".