Two Voices on Aboriginal Pedagogy: Sharpening the Focus
Bibliographic record
Abstract
This paper is the story of the authors’ paths to the shared realization that the strategies and epistemological underpinnings of Aboriginal education need to move out of the margins and into the centre of education in Canada, not only for Aboriginal students, but for all students. Between August, 2010 and April of 2012, the authors were seconded for two years from their Vancouver classrooms to work as Faculty Associates in the teacher preparation program at Simon Fraser University. There we came face to face with the British Columbia Teacher Regulation Branch’s mandate that Aboriginal education courses must be taught to pre-service teachers. Part of our job was to cultivate strategies using Aboriginal pedagogy to inform pre-service teachers’ developing practice and ways of communicating with their students. Here we describe how, after returning to our school district, we changed our teaching practices through actualizing Aboriginal pedagogy.
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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 | 0.022 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.049 | 0.084 |
| Scholarly communication | 0.025 | 0.019 |
| Open science | 0.004 | 0.024 |
| Research integrity | 0.013 | 0.036 |
| Insufficient payload (model declined to judge) | 0.004 | 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".