Casey Would! Dr Casey A. Wood, the McGill Library’s Best Forgotten Benefactor
Bibliographic record
Abstract
The world-renowned physician, bibliophile, and McGill alumnus Sir William Osler (1849-1919) wrote that “a library represents the mind of its collector, his fancies and foibles, his strength and weakness, his prejudices and preferences.”[1] His own collection of medical and related works formed the nucleus of McGill’s Osler Library of the History of Medicine. Opened in 1929, the Osler Library is renowned for both its collection and the preservation of its founder’s memory and ethos. In 1920 Dr Casey Wood (1856-1942), a friend and contemporary of Osler’s, created the Wood Library of Ornithology and the Blacker Library of Zoology at McGill. The rare natural history material from these two libraries, now the Blacker-Wood natural history collection in Rare Books and Special Collections, rivals the Osler in its depth and rarity. Despite this, its founder remains largely unknown. Although it is understandable – Osler was as famous and loved as Wood was aggressive, overbearing and vain – it is unfortunate. Wood devoted tremendous energy, drive and money over decades to build one of the best natural history collections around. He also hoped that these libraries would engage in public education and encourage people to respect and enjoy the natural world. Although he could be an unpleasant person, it is useful to know something about the man whose collection so strongly bears his stamp. [1]Bibliotheca Osleriana: A Catalogue of Books Illustrating the History of Medicine and Science (Oxford: Clarendon Press, 1929), xxi.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.176 | 0.066 |
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".