Relational Approach Towards Decolonizing Curriculum Development within the Colonial Postsecondary Institution
Bibliographic record
Abstract
The Canadian academy is dominated by Western epistemologies that devalue Indigenous ways of knowing and marginalize Indigenous communities, cultures, and histories (Louie et al., 2017). This paper draws on a cross-disciplinary, interprofessional collaboration between a School of Public Health Sciences and School of Social Work to develop an online graduate course that sought to advance knowledge and practice in Indigenous wellbeing and health through a social justice lens. We explore key considerations, strategies, and challenges undertaken by an interdisciplinary group of non-Indigenous professors to create a learning experience for students that challenges colonial ways of seeing, being, knowing, and doing in the professional practice fields of public health and social work and that serves to elevate and sustain Indigenous voices, knowledges, sciences, and practices within the academy. In doing so, we centre the process of course development, including working with an Indigenous Advisory Circle and Indigenous contributors of content, guest lecture videos, and artwork. The paper describes the creation of a relational teaching and learning community, while raising concerns about the institutionalization of this approach to Indigenous-focused course development in the absence of the structural changes needed to enhance the presence of Indigenous faculty and Elders in academic institutions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.013 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".