Exploring the Concept of ‘Decolonized Teaching’ in Higher Education
Bibliographic record
Abstract
Our paper presents a study that explored decolonizing teaching praxis at a prairie university in Canada in which the focus of ‘decolonized teaching’ emerged as a major point of discussion. It is this concept we believe should be discussed and explored in greater detail. Using 30 in-depth qualitative interviews, this study revealed four major themes in the area of decolonized teaching: (1) participants providing diverse definitions of decolonization; (2) participants’ efforts to decolonize their curricula; (3) participants’ understanding of helpful qualities of teachers as they connect to decolonized teaching; and (4) participants’ mixed thoughts on whether partial or total decolonization of higher education, university in particular, is attainable. Taken together, we contend that it is critical for higher education to place an emphasis on decolonized teaching to promote self-growth for teachers and students in and across the post-secondary educational sector.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".