Decolonization in Engineering Education
Bibliographic record
Abstract
Decolonization has emerged as a sociopolitical movement in response to the embedded and ongoing practices of coloniality within the modern state. However, the notion of decolonization remains rather contested, particularly within education. A significant absence of decolonization within the STEM literature has been noted. The purpose of this chapter is to explore how decolonization is conceptualized within the engineering education literature. We discuss the varied meanings of decolonization and identify four categories. Drivers for, and barriers to, engaging in decolonial work within engineering education are considered, and recommendations made. Decolonization requires a fundamental shift in thinking about whose knowledges and belief systems are accessed and how knowledge is acquired and shared. It challenges the notion of objectivity, the purposes and practices in engineering, and the perpetuation of the status quo. It requires centering Indigenous and non-Western methodologies, pedagogies, and practices alongside Western engineering methods, education, and research. Understandings and inclusions of Indigenous and non-Western epistemic, ontological, and axiological worldviews are needed to further drive genuine decolonization in engineering education and research.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".