Knowledge Liaisons: Negotiating Multiple Pedagogies in Global Indigenous Studies Courses
Bibliographic record
Abstract
Over the past few years, Canadian universities have been at the forefront of institutional changes that identify Aboriginal people, internationalization, and pedagogical change as key areas for revision. Most universities’ strategic planning documents cite, at least to varying degrees, these three goals. Institutions have facilitated these changes by supporting new programs, teaching centres, and course redevelopment. While much attention has been given to those goals individually, it is rarely considered how these commitments converge in particular course offerings. This article considers the connections among Indigenous, global, and pedagogical goals by examining undergraduate comparative Indigenous studies courses, some pedagogical challenges that arise in those courses, and some strategies I have developed in meeting those challenges. Based in auto-pedagogy and a critical analysis of existing and emerging pedagogical frameworks, this article uses key concepts from Indigenous epistemologies, knowledge translation, and Sue Crowley’s (1997) levels of analysis to propose “knowledge liaisons” as a teaching model that addresses these challenges.
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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 | 0.034 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.026 | 0.040 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".