Bibliographic record
Abstract
This book has been a long time in the making, far longer than I would have imagined at the beginning of the process.It is difficult to summarize and acknowledge all of the people who, over the years, have influenced the project directly or indirectly.If I've left someone out, it is an oversight, one that I hope to correct in person at some point in the future.I would like to thank the incredibly helpful, supportive, kind, and professional people at the University of Toronto Press.Thanks, first, to Mark Thompson for guiding the manuscript through the many rounds of reviews and approvals.Mark offered encouragement and exceptionally helpful advice when it mattered most, and I am very grateful to him.Barbara Tessman provided insightful and incisive edits, which resulted in a much improved manuscript.Thanks to Frances Mundy for shepherding the manuscript through the latter stages of the process, and also to Breanna Muir for her expert advice and suggestions.I would also like to thank Siobhan McMenemy whose comments early in this process had a lasting impact on the
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.302 | 0.271 |
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".