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Record W4416054971 · doi:10.1163/2208522x-bja10072

Gauging Anger and Qualifying Forgiveness in Late Medieval Summae Confessorum

2025· article· en· W4416054971 on OpenAlexaff
Marc B. Cels

Bibliographic record

VenueEmotions History Culture Society · 2025
Typearticle
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsAthabasca University
Fundersnot available
KeywordsForgivenessAngerConfession (law)MoralityWrongdoingImprisonmentFeeling

Abstract

fetched live from OpenAlex

Abstract Latin manuals of casuistry ( summae confessorum ) for priests hearing confession from the turn of the fourteenth century to the turn of the sixteenth century offer sources for the history of ethical teachings about emotions such as anger in medieval Europe. These summas are casuistic in the sense of offering practical moral reasoning about anger and forgiveness based on the study of cases from Church law and theology. Whereas more public religious exhortations in sermons and treatises on the vices denounced anger and demanded forgiveness with little qualification, the summae confessorum provided a method to gauge the relative morality of anger confessed by penitents and admitted that Christian forgiveness is conditional. This study examines how the discussion of anger developed along with the genre as compilers synthesised and reorganised material from more academic sources, especially that of Thomas Aquinas, to make it more accessible to priests hearing confessions in medieval Catholic Europe.

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 score0.370
Threshold uncertainty score0.685

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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.313
Teacher spread0.288 · 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
Published2025
Admission routes1
Has abstractyes

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