Gender, Fate and McGill University’s Medical Collections: e Case of Curator Maude Abbott
Bibliographic record
Abstract
Let us begin by looking at a group photograph dated 1905. It was taken when the Faculty of Medicine at McGill University in Montreal was establishing its international reputation and possessed one of the largest collections of anatomical and pathological specimens in North America (see Figure 4.1). The lecturer was Canadian-born William Osler (1849-1919) who was, in his time, the best-known North American figure in medicine. A graduate of McGill and its first full-time medical faculty member, Osler was idolized as the ‘father of modern medicine’ by two generations of medical students and practitioners.1 His quest was to bring high standards and scientific methods into general practice by promoting teaching hospitals and medical museums as authoritative places in the training and education of doctors. This photograph was taken in the newly built surgical amphitheatre at the Royal Victoria Hospital in Montreal. There, students had the opportunity to develop observational skills necessary for looking at patients: they were to take seeing and knowing the body as its focal point and its common objective.2
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.045 | 0.034 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".