Bibliographic record
Abstract
and approachable books on medieval people both high and low. She was one of the first scholars to celebrate the broad cultural achievements of medieval women and was a champion of readable history. The founding president of the Canadian Soci-ety of Medievalists / Société canadienne des médiévistes, a fellow of the Royal Soci-ety of Canada, a member of the Order of Canada, and the recipient of three honorary degrees, she was renowned not just for her work on the Middle Ages but also for the boundless energy of her volunteer work on behalf of the elderly. She was, in the words of one fellow medievalist, “a woman of warmth, wisdom, and serenity.” In her introduction to her first book, a biography of Simon de Montfort, Mar-garet praised Charles H. McIlwain and Charles H. Taylor, her undergraduate teach-ers at Harvard, and Sir Maurice Powicke, her mentor at Oxford, for their conviction that “the history of the Middle Ages can be both intelligible and fascinating.”1 That conviction was one she held herself. I first met Margaret (I never felt quite up to call-ing her Polly) some thirty years ago at a lecture she gave at the Pontifical Institute of Mediaeval Studies explaining how she came to write popular history. I was inspired, as were so many others, by her efforts to reach out to a broad readership without betraying the complexity of her subject. With characteristic modesty, Margaret attrib-uted her decision to write for the non-specialist reader to the simple recognition that family responsibilities did not allow her to conduct sustained original research in the
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.491 | 0.317 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".