Indigenizing Engineering education in Canada: critically considered
Bibliographic record
Abstract
This article critically considers the work being done to bring Indigenous Peoples, Knowledges, and perspectives into the dominant structures of engineering education in Canada. We use Gaudry and Lorenz’s (2018. “Indigenization as Inclusion, Reconciliation, and Decolonization: Navigating the Different Visions for Indigenizing the Canadian Academy.” AlterNative: An International Journal of Indigenous PeoplesAlterNative 14 (3): 218–227. doi:10.1177/1177180118785382) spectrum of Indigenization to evaluate self-reported contributions from 25 engineering programs and four engineering organizations. Findings show much of the work being done in Canada is in Indigenous Inclusion and Reconciliation Indigenization, with some Decolonial Indigenization. Efforts in reconciliation and decolonization are seen predominantly in integrated, grassroots initiatives, with institutional initiatives found largely in inclusion. We submit that a diversified strategy and decolonized policies are needed to achieve Decolonial Indigenization. The intention of this work is to create an ethical space where Indigenous and non-Indigenous engineering educators can listen to and learn from one another. Guided by Etuaptmumk (Two-Eyed Seeing), we can advance Indigenous ways of knowing, being, and doing in engineering education in Canada and around the world.
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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 | 0.018 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.066 | 0.041 |
| Scholarly communication | 0.023 | 0.005 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".