Education for reconciliation: Examining the effects of an Indigenous course requirement on non-Indigenous students’ attitudes
Bibliographic record
Abstract
In the years following the Truth and Reconciliation Commission of Canada’s (TRC) Final Report (TRC, 2015), educators, administrators, and policymakers across the country have wrestled with the question of how formal teaching and learning can help to establish and maintain a mutually beneficial relationship between Indigenous and non-Indigenous people in Canada (Kairos, 2016). This study examines this issue in the context of The University of Winnipeg, one of the nation’s first institutions to require all undergraduate students to take an Indigenous Course Requirement (ICR). This mixed-methods study examined the impact of select ICR courses on non-Indigenous students’ attitudes towards issues of reconciliation. Using the framework of disruptive knowledge (Kumashiro, 2000; Regan, 2010), this study examined ICR courses that emphasized the ongoing discrimination facing Indigenous peoples in Canada (TRC, 2015). Drawing on survey data ( n = 50) and in-depth interviews ( n = 8), this study revealed several positive outcomes of these courses: increased recognition of discrimination facing Indigenous peoples, increased support for government initiatives, and self-described behavioural changes. At the same time, this study highlights the limits of such courses within the broader work of reconciliation in a settler-colonial context. Implications for policy and practice will also be discussed.
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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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".