“Dear brother-in-trouble” Casey Wood’s enduring presence in the Osler Library of the History of Medicine
Bibliographic record
Abstract
When Casey Wood became William Osler’s first clinical clerk at the Montreal General Hospital in 1877, it would have been impossible to foresee the impact that pairing would have on collections development at the McGill University Library. Osler inspired Wood’s bibliophilia and guided him to focus his collecting, resulting in the establishment of specialised collections: on ophthalmology within the Medical Library and on ornithology and zoology within the McGill University Library. Although Casey Wood’s gifts to the Osler Library were limited and carefully selected during his lifetime, after his death the ophthalmological collection was transferred to the Osler Library of the History of Medicine, along with other works relating to medicine. These books, combined with documentation contained within a number of archival collections at the Osler Library, mean that Casey Wood can truly claim to have an enduring presence in the Osler Library. While the books speak to part of Wood’s legacy at McGill, the archival documentation speaks to his character: as a man who was determined and focused, but who also expressed racist views and fascist sympathies.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".