Bibliographic record
Abstract
I want to begin by acknowledging that this research was completed and this manuscript was written on the traditional territory of many Indigenous Nations.The area known as Tkaronto has been caretaken by the Anishinabek Nation, the Haudenosaunee Confederacy, the Wendat, and the Métis.The current treaty holders are the Mississaugas of the New Credit First Nation and this territory is subject to the Dish with One Spoon Wampum Belt Covenant, an agreement to peaceably share and care for the Great Lakes region.This land remains home to many Indigenous Peoples and, as a settler within Canada, I am continuously trying to understand how to best fulfill my commitment to the Indigeneity of this place in my teaching, research, and writing.In 2015, almost four years since finishing research I conducted while at York University (Toronto), I felt that it was time to go back.I had made a commitment to the students with whom I worked to ensure that their voices would be heard and would have an impact on how we teach and learn Canadian history.I felt this was the moment to fulfill that commitment.I also felt enough time had passed to enable me to feel less angry and confused about situations I had encountered during my research.With the passage of time, I began to understand these feelings as important rather than as simply reactionary.I also knew that what I wanted to say was crucial to the conversation about Canadian history and identity -even though this was not so clear in the context of the multiple aims of my research.And so I
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.256 | 0.166 |
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".