Bibliographic record
Abstract
My name is Aven McMaster and I am a settler of European ancestry. My father came to Canada from England when he was twelve, and my mother was born in Toronto, although her family came from England and Ireland at various points in the twentieth century. I was born in Ottawa, Ontario, Canada on the traditional, unceded territories of the Algonquin nation, but did not know the history of that land when I was growing up. I now live and work in the city of Sudbury, Ontario, Canada on the traditional Anishinaabe territory of the Atikameksheng Anishnawbek and Wahnapitae First Nation, within the boundaries of the Robinson Huron Treaty of 1850 . Until recently I was a professor of Ancient Studies, and I come to the work discussed in this chapter after years of slowly realizing how harmful and destructive the history of my discipline has been, and how embedded it is within the colonial structures of Canada as a settler state and the university as a colonial institution. I also come to this work with the help of Indigenous colleagues and students who have aided me to see some of the unlearning I need to do, and the changes that I need to be part of making. 1
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".