Weaving Indigenous knowledge into the academy : promises and challenges from the perspectives of three Aboriginal post-secondary institutes in British Columbia
Bibliographic record
Abstract
This study examines the promises and challenges of integrating Indigenous Knowledge (IK) into the academy from the perspectives of Elders, leaders, students, staff, and instructors from three Aboriginal post-secondary institutions in British Columbia. Using a case study method and an Indigenous and Western theoretical foundations, this research shares the perceived successes, limitations, and the challenges the Nicola Valley Institute of Technology (NVIT), the Wilp Wilxo'oskwhl Nisga’a Institute (WWNI), and the former Cariboo Chilcotin Weekend University (CCWU) program face, or have faced, in the integration of IK. Also included in this study are perspectives from individuals from one mainstream, non-Aboriginal institution, the University of Northern British Columbia (UNBC). Topics explored through the research are the following: a) challenges and benefits of integrating IK in three Aboriginal institutes and how the integration of IK at the academic level in Aboriginal institutions impacts and benefits students, staff, and the local community; b) the challenges and benefits of partnerships with mainstream institutes; and c) the formal policies and/or lack of formal policy for Aboriginal institutes. As a result of the research, emerging themes include: Elders have a core role in higher learning; the integration of IK at a post-secondary level impacts higher learning; Aboriginal post-secondary institutes have taken the lead in building partnerships with post-secondary institutes; and Aboriginal post-secondary institutes demonstrate resiliency despite systemic challenges. To represent my position as a Métis scholar I present my findings through the framework of the Métis Sash that represents through its colour and design the integration of key concepts and findings from the study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.049 | 0.012 |
| Scholarly communication | 0.010 | 0.002 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.002 | 0.005 |
| 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".