Bibliographic record
Abstract
Over the past ten years, I have been privileged to devote much of my time to learning about the rich history of Indigenous women in Canada.I drew a lot of inspiration from stories of women's dynamic twentieth-century experiences, be they told to me in person, in books, or through the sometimes lucid but often messy, complicated and veiled archival record.The women whose lives I have studied worked with integrity in their given fields and strived to nurture the lives of their loved ones and advocate for their families and communities.They made, at times, what must have been difficult personal decisions while seeking out opportunities large and small to effect change in their own lives, their communities, and the world around them.Thinking through the significance of their work has been an honour.This book is the result of many generous conversations, ideas, criticisms, acts of inspiration, and words of encouragement, and I am grateful for the time, work, and friendships that have gone into its making.Many thanks to the individuals who shared their knowledge in the research of this project, including Ann Callahan, Myrna Cruickshank, Eleanor Olsen, and Dorothy Stranger, and a special thanks to Ruth Christie, who took me canoeing around Loon Straits and, over many lunches, visits, and tours, has taught me more about the cultural, social, and political histories of Winnipeg, Selkirk, Lake Winnipeg and the second best river in the world, the Red.Thanks also to the Aboriginal Nurses Association of Canada, Faye Isbister-North Peigan, Rosella McKay, Carol Prince, Marilyn Sark, and Marilyn Tanner-Spence for their time in the history of nurses project.Thank you to those who made this research possible, Ryan Eyford, Mary Young, Leslie Spillett, and Judith Bartlett, and to Margaret Horn, then at the Aboriginal Nurses Association of Canada, and Debbie Dedam-Montour at the National Indian and Inuit Community Health Representatives Organization for their help and interest.
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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 | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.424 | 0.310 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".