Indigenous Knowledge in Post-secondary Educators' Practices: Nourishing the Learning Spirit
Bibliographic record
Abstract
From 2006 to 2009, Indigenous Elders and scholars shared their insights in the Comprehending and Nourishing the Learning Spirit Animation Theme Bundle of the Aboriginal Learning Knowledge Centre (ABLKC). The ABLKC was an applied research, knowledge exchange, and monitoring program with a mandate to advance Aboriginal education in Canada. One of the six bundles, Nourishing the Learning Spirit, was led by Mi’kmaw education scholar and Academic Director of the Aboriginal Education Research Centre at the University of Saskatchewan, Dr. Marie Battiste. In this paper, the authors discuss how they applied knowledge gained in the Nourishing the Learning Spirit Animation Theme Bundle to their post-secondary classroom practice. The authors argue that teachers are better able to nourish the learning spirit of students when they understand themselves as lifelong learners, validate and learn from their students, and use holistic teaching pedagogies.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".