Barriers to Decolonization in Post‐Secondary Education: Reflections From Non‐Indigenous Faculty Across the Disciplines
Bibliographic record
Abstract
ABSTRACT In the territory currently known as Canada, the work of decolonization, Indigenization, and reconciliation within postsecondary institutions is understood as the work of all educators, both Indigenous and non‐Indigenous. Yet non‐Indigenous faculty often struggle to engage, if they engage at all. This article explores barriers for non‐Indigenous faculty to engage in decolonization through analysis of conversations with seven non‐Indigenous faculty at a mid‐sized Canadian university who participated in Disrupting interviews. By analyzing themes in participants’ initial interviews, I identify four barrier categories that impede non‐Indigenous faculty from engaging in the necessary work of decolonization: structural and institutional barriers, disciplinary barriers, individual barriers, and conceptual barriers related to understandings of the relationship between humans and the land. I use faculty comments from individual interviews to nuance each general theme and connect their ideas to broader work on decolonization and Indigenization of higher education. In doing so, this article further demonstrates the usefulness of Disrupting interviews for both uncovering barriers and motivating change among non‐Indigenous faculty specifically and offers those working in post‐secondary institutions important data that will help better support non‐Indigenous teaching faculty to engage in their responsibilities in this area.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.027 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.007 |
| 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".