Bibliographic record
Abstract
I should like to thank four people for their help.The first is Michael Harrison, from the University of Toronto Press, who sat in my office one day a couple of years ago and casually asked how my courses were going.His reward for such politeness was an extended grumble about how much harder it was to teach Charter law now that the novelty has worn off, the big questions are all answered, and the news headlines no longer remind students how exciting and current things are-at the end of which Michael simply said, "That sounds like a book to me." Quite so, and I had a formal proposal in his email inbox by the time he got back to Toronto, but I am not sure the grumble would have become a book without the nudge.The second is Rainer Knopff of the University of Calgary, who was supposed to be an anonymous assessor who would briefly say whether or not the manuscript was publishable, but who switched instead into pen-in-hand professor on the hunt for things that were not quite right, of which he found a fair number; and he voluntarily relinquished his anonymity to allow me the chance to argue back.Some were just "oops" mistakes, some were unclear ideas that needed tightening, some were points on which we still disagree but I gained from knowing how to focus my argument.This book is much the better for Rainer's generosity, which is not to say that the arguments won't resume next time we meet.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.456 | 0.345 |
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".