Introduction to Special Issue on Casey Albert Wood
Bibliographic record
Abstract
Shortly after his retirement from medical practice in 1920, Dr. Casey Albert Wood (1856–1942), a Canadian-born medical doctor, founded the Emma Shearer Wood Library of Ornithology and the Blacker Library of Zoology at McGill University. These libraries eventually merged to form the Blacker Wood Library of Ornithology and Zoology, which is now the Blacker Wood Natural History Collection. I first encountered this remarkable collection and its long-serving librarian Eleanor MacLean (1947–2018) in the early 1990s. For thirty years, until her retirement in 2011, MacLean had ensured that researchers had access to the treasures preserved in one of North America’s finest special collections for the study of natural history. MacLean was heir to a line of dedicated librarians going back to Gerhard Lomer (1882–1970) and Henry Mousley (1865–1949), both of whom worked with Casey Wood to build these libraries.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.232 | 0.105 |
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".