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Record W3021037086 · doi:10.3726/med012018_261

<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.

2018· article· en· W3021037086 on OpenAlexaboutno aff
Albrecht Classen

Bibliographic record

VenueMediaevistik · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicReformation and Early Modern Christianity
Canadian institutionsnot available
Fundersnot available
KeywordsHonorTributeScholarshipLegendClassicsHoly GrailHistorySociologyArtArt historyLaw

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.525
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.257
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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