Untying a Dreamcatcher: Coming to Understand Possibilities for Teaching Students of Aboriginal Inheritance
Bibliographic record
Abstract
Increasing the number of Aboriginal students graduating from university is a goal of many Canadian universities. Realizing this goal may present challenges to the orientation and methodology of university curricula that have been developed without consideration of the traditional epistemologies of Aboriginal peoples. In this article, three scholars in the Faculty of Education at the University of Victoria take up this issue by dialoguing with each other about the possibilities of incorporating Aboriginal perspectives into their courses. These conversations are woven together into the narrative form of a four-act play in which the authors caricature their personalities to highlight their initial resistances and eventual reconsiderations. As non-Aboriginal instructors from different cultural backgrounds, the authors confront issues of respect, responsibility, and (mis)representation as they struggle with the dilemmas involved in cross-cultural understanding. Through this journey they come to imagine a world where cultural differences, including the traditional epistemologies of Aboriginal peoples, present possibilities for greater understanding of each other and more authentic expressions of our humanity.
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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 | 0.023 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.052 | 0.063 |
| Scholarly communication | 0.017 | 0.016 |
| Open science | 0.004 | 0.022 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.005 | 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".