Decolonizing Engineering Education: Tensions, Contexts, and Commitments Across Four National Settings
Bibliographic record
Abstract
This essay stories how four scholars understand colonization and how its continued impact on colonized peoples and Lands relate to engineering, and engineering education and research in Australia, Canada, South Africa, and the United States. Genuine movement toward decolonization in engineering education and research should include substantial partnerships, collaboration, and shared decision-making with Indigenous and non-dominant peoples, and the centering of Indigenous and non-dominant worldviews, and it must be localized. Decolonization challenges nationalism, capitalism, and landownership, and the notions of objectivity and the purposes and practices in engineering. Decolonization requires a fundamental shift in thinking about whose knowledge and belief systems are accessed, and how knowledge is acquired and shared. Engineering educators need to understand the crucial value of Indigenous and non-dominant knowledges and worldviews for engineering, the urgency for advancing social justice, and the responsibility of our profession to take up the call and work for decolonization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.031 | 0.033 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.002 | 0.005 |
| 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".