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Record W1530750122 · doi:10.3138/flor.29.006

Anger in Thomas of Ireland’s <i>Manipulus florum</i> and in Five Texts for Preachers

2012· article· en· W1530750122 on OpenAlexaffvenue
Marc B. Cels

Bibliographic record

VenueFlorilegium · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsAthabasca University
Fundersnot available
KeywordsOrder (exchange)AngerLiteratureClassicsHistoryMiddle AgesPhilosophyTheologyArtPsychologySocial psychology

Abstract

fetched live from OpenAlex

Thomas of Ireland’s Manipulus florum was the most famous dictionary of quotations in the late Middle Ages. Its main critical expositors, Richard and Mary Rouse, characterized it as one of the most successful reference works for providing medieval preachers with authoritative citations to support the arguments in their sermons. The Rouses highlighted the florilegium’s convenient, searchable format, which organized quotations under alphabetically arranged headings. Recently, Chris Nighman, in examining the manual’s material, has questioned the Rouses’ conclusions about Thomas’s original intention, his audience, and even the suitability of his florilegium for popular preaching. At stake is the use of the Manipulus florum for the study of what teachings were considered preachable and, probably, what was most often preached. In order to test the Rouses’ view and Nighman’s revision, this study compares the ways in which the same topic, namely, anger (ira), is discussed in the Manipulus florum and in a sample of widely-circulated Latin thirteenth- and early fourteenth-century reference works that provided elements for the composition of sermons: William Peraldus’s Summa de vitiis, the Distinctiones Mauritii, the Liber Pharetrae, Bartholomew of Pisa’s De documenta antiquorum, and Peter of Limoges’s Tractatus moralis de oculo. Many widely copied model sermons and even a few transcripts of preached sermons survive, especially those of accomplished preachers. However, the more humble preaching manuals are likely to provide a better indication of commonly preached content than more polished sermons that may never actually have been preached.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.521
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.027
GPT teacher head0.228
Teacher spread0.201 · 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 designNot applicable
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
Published2012
Admission routes2
Has abstractyes

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