Review of <i>Braiding Histories: Learning from AboriginalPeoples' Experiences and Perspectives</i> by Susan Dion
Bibliographic record
Abstract
In its final report in 1996, the Royal Commission on Aboriginal Peoples observed that Canadians have little knowledge of Aboriginal people, the issues of importance to them, and the history that underlies Aboriginal-non-Aboriginal relationships today. How can this be changed? In Braiding Histories, Susan Dion takes up the complexities of transforming the consciousness of non-Aboriginal people through education. The book is organized around three focal points. First, the author and her brother Michael Dion {re)write and {re}tell the life stories of several Aboriginal people, including Beothuk survivor Shanawdithit, the Plains Cree leader Mistahimaskwa, and the writers' mother, Audrey Dion, who grew up on the Moravian of the Thames Reserve in Ontario. The stories are rigorously constructed to challenge common stereotypes and to create possibilities of discovery for the reader. The particular concerns of the storytellers are to reveal the humanity and agency of Aboriginal people and to encourage non-Aboriginal readers to recognize their own connection as Canadians to the historical and continuing oppression of Aboriginal people. Second, Dion outlines in detail her "Braiding Histories Project." In this study, she analyzes the teaching of two of the stories by three intermediate grade non-Aboriginal teachers. Third, Dion shares her own efforts to teach a graduate course called "Teaching and Learning from Indigenous Ways of Knowing" to teachers.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.019 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".