Bibliographic record
Abstract
First and foremost, our thanks go to the thousands of Indian residential school survivors and their families whose stories we had the privilege of listening to at the Truth and Reconciliation Commission national events.We also want to acknowledge the TRC commissioners and organizers who carried out the momentous task of implementing their part of the Settlement Agreement-and who provided us with the opportunity to think much more deeply about the possibilities and problems of reconciliation.The TRC's hard-hitting summary report and in particular the ninety-four calls to action are deserving of our utmost attention.We would like to thank the Social Sciences and Humanities Research Council of Canada for the funding that allowed us to form a research collective and to attend several of the TRC national events.We also acknowledge the support of the European Research Council for their funding of the Indigeneity in the Contemporary World project at Royal Holloway, University of London.We are also grateful to Petah Inukpuk, to Avataq Cultural Institute, to the Provincial Archives of Saskatchewan, and to the Truth and Reconciliation Commission of Canada for allowing us to use the various images included in this collection.To all of our co-researchers and contributors, we thank you for the privilege of our many conversations and for the important and challenging work that you are carrying out.Our thinking was enriched by several people whose work is not represented in this collection, but whose presence at our gatherings was integral to the evolution of this book: Helen Gilbert,
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.245 | 0.174 |
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".