Bibliographic record
Abstract
Toni Healey would frequently, with some pride, tell her colleagues at the Dictionary of Old English ( DOE ) that the young Antonette diPaolo grew up in Kennett Square, Pennsylvania (Mushroom Capital of the World), though she had been born in Baltimore, Maryland, and so, arguably, is a Southern belle. Perhaps these auspicious beginnings presaged a career of great distinction. Toni graduated with a Bachelor of Arts in English in 1967 from the College of New Rochelle, New York, and continued her studies at the University of Toronto, where she completed a Master of Arts in 1969 and then a PhD in 1973 under the supervision of Angus Cameron. After a year as a lecturer in the Department of English at the University of Toronto she took up a position for four years as an assistant professor in the Department of English, Yale University. Fortunately for the future of Old English lexicography, Professor Cameron, the inspiration behind the Dictionary of Old English and its founding editor, was able to persuade her to come back to Toronto to join the editorial team he was in the process of assembling, and she returned in 1978, along with her husband Robin and their then infant daughters, Elspeth and Emma. She was successively an assistant editor, an associate editor, a co-editor (with Ashley Crandell Amos), and then Chief Editor from 1989 until her retirement in 2014. This tribute to Toni will of necessity be a mere sketch of her achievements and contributions to scholarship in her almost forty years at the DOE and the University of Toronto. Her overview of the DOE was unparalleled; she was a participant in the very early days of planning and the building of the research collection, publishing, in advance of the release of the first letter, D , in 1986, works on the plan for the DOE , on the microfiche concordance, on the electronic corpus, and on the design of the computer system. She constantly anticipated the next direction needed for every aspect of the project – editorial, technological, and financial. She scheduled the writing and revising of entries and put her superb analytical skills to use in those she wrote herself. She guarded the electronic corpus zealously, ensuring that it was (as it still is) regularly updated and corrected so that the published Dictionary would be increasingly comprehensive and accurate.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.086 | 0.054 |
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".