Colonial and Indigenous language policies at McGill University: beliefs, mechanisms, and practices
Bibliographic record
Abstract
Universities in Canada have generally perpetuated Eurocentric approaches to language that disproportionately value English and French, to the detriment of Indigenous languages. National calls to action about Indigenous languages in higher education seem to contradict institutional ones developed at McGill University. This study aims to amplify Indigenous voices and map institutional processes at McGill. My research explores two main questions: (1) What do Indigenous people at McGill have to say about the University’s role in Indigenous language revitalization (ILR)? (2) How do accreditation processes at McGill recognise and value students’ Indigenous language abilities, and/or their experience with language immersion programs and community based ILR? I have approached these questions from my own insider/outsider standpoint as a Métis graduate student in the Second Language Education program (M.A.) at McGill. Following a preparation stage, data collection began with an initial phase of consultations with other Indigenous people at McGill (conversational method, n=6). After the issue of accreditation was raised by these consultants, I began talking with McGill staff and faculty (most of whom were non-Indigenous) and engaged in textual analysis to map out McGill’s course equivalency process. I also recorded field notes of my observations and experiences on campus with a focus on language policy. My analysis explains how work is coordinated throughout the institution to discount learning experience with non-accredited community-based institutions. Despite the lack of formal recognition (e.g., course credits), Indigenous staff, students, and faculty continue to bring their languages with them, gradually increasing their presence and visibility on the University campus. As stakeholders throughout the institution continue to respond to sometimes contradictory calls to action, this study maps some of the terrain and identifies some of the mechanisms that bridge the gap between ideology/belief and practice.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.030 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".