Bibliographic record
Abstract
It seems that the most useful prefaces serve as a place of introductionand not just to the subject matter, but also to the writer who situates herself in the work.In Indigenous Methodologies, Margaret Kovach (Nêhiýaw/Saulteaux) recalls Māori scholar Graham Smith's instructions to begin with a prologue.The idea, she says, is to narrate for readers the information required to "make sense of the story to follow." 1 As Kovach's own prologue explores, it can be difficult to narrate the origins of one's work.But one can hopefully say something about the relationships-to people, places, and stories-that have informed that work.While I was revising this manuscript with the feedback of my reviewers, my ten-year-old stepdaughter asked me: when did I first "find out" that I had to write this book?The phrasing charmed me; I had the humorous image of myself receiving surprise notification in the mail.But this was a really good question, and its phrasing intuited something important about research and writing: that to whatever extent we choose our work, it also chooses us.This is not to efface our personal accountability as academics and teachers, but to say that many factors of history, social relations, and time converge and give shape to the choices we make.Though curious about the topic of the book, my stepdaughter was more interested in just that-in how and why it had become a daily labour for me.At first, I explained how the book began as work written for a doctoral degree while I was studying at Western University in London, Ontario, and that I had been reworking the project since moving to the Okanagan and taking up my job in Kelowna as a teacher at UBC.But this isn't really what she meant.It wasn't so much a timeline she was looking for, as a sense of those larger convergences: why me, vii
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.585 | 0.386 |
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".