Faculty Perspectives about the Impacts of Implementing Indigenous Content at Humber College in Ontario, Canada
Bibliographic record
Abstract
This qualitative, multi-method study examined course outlines through a Qualitative Document Analysis (QDA), which then led to the Qualitative Interviews (QI) of faculty members who taught Indigenous content within their academic courses. The initial analysis focused on 180 course outlines from 8 different academic programs in the Faculty of Social and Community Services at Humber College. Degree and diploma program course outlines were included in the analysis, which was centred on locating Indigenous content within each of the 8 academic programs. The QI explored the perspectives of 12 professors in the Faculty of Social and Community Services at Humber College who had taught courses that had embedded Indigenous content. These interviews sought to understand the perspectives of professors about the challenges, success, and motivations to include Indigenous content in their curricula. Through the QDA and QI, critical themes were uncovered, and emphasized the importance of the relationships developed within the classroom setting between professors and students, and the diverse culture that each post-secondary classroom offers learners. Each academic program contained a different level of Indigenous content, and the professors offered critical insights into what content was taught and what the impacts of this learning were within the classroom. The professors who had integrated Indigenous content into their pedagogy and curricula demonstrated Culturally Responsive Teaching practices and were engaged in a variety of ways with Indigenous content and the 94 Calls to Action of the Truth and Reconciliation Commission. Professors’ approaches to teaching and learning, commitments to social justice, and interest in the decolonization and Indigenization of curricula had the most significant impacts on the implementation of Indigenous content.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.035 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".