Towards Defining Threshold Concepts for Decolonizing Engineering
Bibliographic record
Abstract
The Calls to Action published by the Truth and Reconciliation Commission of Canada express a need for post-secondary institutions to integrate Indigenous knowledge and teaching methods into the classroom. In response, engineering programs across Canada have started to explore what decolonization and Indigenization might look like in the context of engineering education. Moodie [20] has theorized five threshold concepts that may be useful for understanding learning in the context of Indigenous Studies for non-Indigenous students and faculty. This paper presents an analysis of threshold concepts for student learning around decolonizing engineering, using secondary accounts of two engineering students’ experiences with Indigenous Knowledges and worldviews in an engineering classroom. We explore how incorporating Indigenous Knowledges in engineering education may require students to cross ontological and epistemological thresholds.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.009 | 0.067 |
| Scholarly communication | 0.014 | 0.023 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| 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".