Bibliographic record
Abstract
We are delighted to express our appreciation to the long list of people and institutions who have made this edition possible and encouraged us in pursuing it.Foremost are our colleagues and teachers, without whose early and ongoing advice and support we could neither have begun nor completed this project: the late Eric Stanley (with particular thanks for introductions to the Bodleian Library, Oxford hospitality, and bringing two of his students from different sides of the Atlantic together); Linda Voigts, George Keiser, Monica Green, and Peter Jones for their indispensable contributions to our understanding of the history of medicine, especially in England; the late A.G. Rigg for many years of help with Daniel's Latin and Greek sources; Ralph Hanna and Tony Edwards for their helpfully astringent advice at the beginning of the project and Laurence Moulinier-Brogi for her advice near its end; Jake Walsh Morrissey and especially Faith Wallis for their special contributions in relation to Daniel's beta text, herbal knowledge, astronomical and calendric interests, and more esoteric sources.We also gratefully acknowledge the help of colleagues on the MEDMED-L listserv, founded and maintained by Monica Green, in solving some of the knottier puzzles posed by Daniel's authority-citations, and an anonymous reader for University of Toronto Press for suggestions on linguistic aspects of the edition.Errors that remain are our own
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.311 | 0.218 |
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".