Bibliographic record
Abstract
This article explores the concepts advanced from the Australian Learning and Teaching Council (ALTC)-funded project, ‘Exploring Problem-Based Learning pedagogy as transformative education in Indigenous Australian Studies’. As an Indigenous art historian teaching at a mainstream university in Canada, I am constantly reflecting on how to better engage students in transformative learning. PEARL offers significant interdisciplinary theory and methodology for implementing content related to both Canadian colonial history and Indigenous cultural knowledge implicit in teaching contemporary Aboriginal art histories. This case study, based on a third-year Indigenous art history course taught at University of Regina, Saskatchewan in Canada will articulate applications for PEARL in an Aboriginal art history classroom. This content-based course lends itself to an interdisciplinary pedagogical approach because it remains outside the traditional disciplinary boundaries accepted in most Eurocentric-based histories of art. Implementing PEARL both theoretically and methodologically in tandem with examples of contemporary Indigenous art allows for innovative ways to balance course content with the sensitive material required for students to better understand and read art created by Indigenous artists in Canada in the past 40 years.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".