Teaching Aboriginal perspectives: An investigation into teacher practices amidst curriculum change
Bibliographic record
Abstract
This paper reports on a study exploring ways in which five experienced teachers interpreted and responded to a curricular initiative in Alberta calling for teachers to help students see social studies through multiple perspective lenses representing Aboriginal (and Francophone) communities. Over the course of the study, which focused primarily on how the research participants integrated Aboriginal perspectives in their teaching, the teachers generally interpreted and practiced the teaching of multiple perspectives as providing students with alternative viewpoints on contemporary issues. Of note were teachers resistances to affording room for Aboriginal perspectives, and a general absence of engagements with these perspectives in the classroom. I argue that these resistances may stem from the legacy of a collective memory project that has worked to foster a historical consciousness that makes it hard to perceive, as well as acknowledge the relevance of engaging ÔOther perspectives. In response, I draw attention to perspectives unique to Aboriginal traditions and communities and then offer possibilities for how teachers could alternatively conceptualize and take up this curricular mandate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.012 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".