Bibliographic record
Abstract
To all the people in the Native Education Centre who allowed me into their lives in my pursuit of understanding and truth, I am forever indebted.I thank those First Nations students, staff members, and board members who, despite all the misrepresentations which they and theirs have previously endured, continue, with patience and humour, to work with this non-Native researcher.I also thank the non-Native staff who 'fight by their sides' and who also took time from their demanding work to talk with me and to help me.I only hope that somehow my work may prove useful to the enduring struggle in which Native people are engaged: the struggle for justice, control, and mutually respectful power relations.I am grateful to Jane Gaskell, my thesis advisor, for her critical guidance, her relentless encouragement, and her confidence that the women of the world will get the job done.And I am grateful to Elvi Whittaker for taking me to heights of enthusiasm over ethnography -heights from which I hope never to descend.I thank my friend Jo-ann Archibald of the Sto:lo Nation and now the director of the First Nations House of Learning: she allowed me dignity during the long and messy process of getting this work together.With respect to the latter stages of this book's production, I am grateful to Carl Urion of the Metis Nation and the University of Alberta for his loving belief in this work and his clear editorial guidance; to Naomi Stinson, a truly skilled craftsperson/
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.336 | 0.192 |
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".