Braiding Histories: Learning From Aboriginal Peoples’ Experiences and Perspectives, by Susan Dion
Bibliographic record
Abstract
Susan Dion's Braiding Histories: Learning from Aboriginal Peoples' Experiences and Perspectives (2009) emerged from her doctoral research, Braiding Histories: Responding to the Problematics of Canadians Hearing a First Nations Perspective of Post-Contact History.Dion's exploration of the relationship between Aboriginal people and non-Aboriginal people in Canada, and the implications this relationship has on teaching and learning, offers important pedagogical contributions that are useful for curriculum developers, education administrators, teacher educators, and teachers of First Nations content.Dion's work draws attention to concerns with the presentation of First Nations content in school curriculum and, through three Braiding Histories Stories, offers a transformative approach to "the ways in which Aboriginal people are remembered and (re)presented in the school curriculum" (2009, p. 13).This book presents an empirical study of the way that the Braiding Histories Stories were presented by two classroom teachers with predominantly non-Aboriginal students.It investigates the relationship that teacher responsibilities and dominant teaching discourses had on the teaching and learning exchange between the teachers and their students.The Braiding Histories Stories, originally co-written with Dion's brother Michael as part of the larger Braiding Histories project, offer the (re)telling of the life stories of Plains Cree Chief Mistahimaska, Beothuk woman Shawnadithit, and their mother Audrey Dion.Describing the project as a potential for informing a "pedagogy of possibility" (2009, p. 64), Dion maintains that the intention of the stories is to disrupt the dominant approaches that produce and reinforce the notion of Aboriginal peoples as romanticized, mythical Others.Moreover, the author explains that the Braiding Histories Stories were written "in the hope for justice" (2009, p. 47) and contribute to the healing process of Aboriginal people.To strengthen the above articulation, the author weaves in the works of Janice Acoose (1993) and Marie Annharte Baker (1994) who confirm the healing power of stories and the important role they play in advancing "the beauty and strength of First Nations people" (2009, p. 47).Extending these earlier works, the healing power of stories inspired her and Michael to write in a way that promoted healing and recovery:
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 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".