<i>From Learning to Love: Schools, Law, and Pastoral Care in the Middle Ages: Essays in Honor of Joseph W. Goering</i>, ed. Tristan Sharp with Isabelle Cochelin, Greti Dinkova-Bruun, Abigail Firey, and Giulio Silano. Toronto: Pontifical Institute of Mediaeval Studies, 2017, xlviii, 775 pp., 1 frontispiece, 13 color plates.
Bibliographic record
Abstract
This mighty Festschrift is dedicated to the highly influential teacher and scholar Joseph W. Goering at the Pontifical Institute of Mediaeval Studies, Toronto, who supervised nearly forty doctoral dissertations and helped an entire generation of theological and historical medievalists to complete their education and to find employment, for which he received the CARA teaching award in 2015 from the Medieval Academy of America. He himself was a student of, for instance, famous Leonard Boyle, and translated, together with Frank Mantello, the letters of Robert Grossetest into English. He also published the highly regarded The Virgin and the Grail: Origins of a Legend (2005), which was translated into French in 2010, to mention just two other titles among his monographs, leaving out eight edited volumes, and fifty-one articles (according to the list of his publications, 736–39; however, the introductory tribute mentions only thirty-eight, xxi). It remains unclear what the specific occasion for this Festschrift might have been, an anniversary or even his retirement (?), although John Van Engen offers an in-depth discussion of Goering’s education, scholarship, and teaching.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".