Bibliographic record
Abstract
BACKGROUND: As the centenary of Sir William Osler's death approaches on 29 December 2019, it is worth pausing to reflect on the relationship between the Osler Library of the History of Medicine at McGill University and the image of William Osler, for the two are arguably inextricable. When Osler died he had not yet completed his library, yet his donation to McGill included nearly 8000 volumes that represented the foundations of western medicine. The Osler Library now boasts over 100 000 titles and is recognised globally as a centre for the study of medical history. METHODS: The approach taken here was to examine inscriptions in the books that William Osler bequeathed to the McGill Medical Facultyin order to learn more about William Osler, the man. RESULTS: By examining inscriptions William Osler and others made in his books, it was possible to learn more about how Osler interacted with his friends, his patients, and also his books. CONCLUSION: It is argued that these inscriptions are as instructive as they are enriching. They reveal information about Osler's priorities and his personal and professional relationships; future scholars will likely find it useful to examine inscriptions more broadly, to gain insight into such topics as the book trade and world events.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.008 |
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".