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Record W563602436

Where heaven and earth meet: essays on medieval Europe in honor of Daniel F. Callahan

2014· book· en· W563602436 on OpenAlexaboutno aff
Michael Frassetto, Matthew Gabriele, John D. Hosler, Daniel F. Callahan

Bibliographic record

VenueBRILL eBooks · 2014
Typebook
Languageen
FieldArts and Humanities
TopicMedieval Literature and History
Canadian institutionsnot available
Fundersnot available
KeywordsHonorHeavenPower (physics)LiturgyHistoryDiplomacyClassicsTheologyHeresyAsceticismAncient historyReligious studiesEnvironmental ethicsArtPoliticsArchaeologyPhilosophyLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Introduction Matthew Gabriele, Virginia Tech Part 1 - Temporal Concerns Chapter 1: Gregory the Great's Gout: Suffering, Penitence, and Diplomacy in the Early Middle Ages John Hosler, Morgan State University Chapter 2: Missing Mancus and the Early Medieval Economy Richard Ring, University of Kansas Chapter 3: of Chabannes as a Military Historian Bernard Bachrach, University of Minnesota Chapter 4: 'Renaissance' vs. the Medieval Papacy: The Cases of Popes Nicholas V (1447-55) and Pius II (1458-62) Lawrence Duggan, University of Delaware Part 2 - Spiritual Concerns Chapter 5: Insular Latin Sources, 'Arculf,' and 'Islamic Jerusalem' Lawrence Nees, University of Delaware Chapter 6: Customs Confirmed by Reason and Authority': The Function and Status of Houses of Canons in Tenth-Century Aquitaine Anna Trumbore Jones, Lake Forest University Chapter 7: of Chabannes and the Peace of God Michael Frassetto, University of Delaware Chapter 8: Liturgy, Its Music, and Their Power to Persuade James Grier, University of Western Ontario Chapter 9: Female Religious as Collectors of Relics: Finding Sacrality and Power in the Ordinary Jane Schulenberg, University of Wisconsin, Madison Chapter 10: Heresy and the Antichrist in the Writings of Ademar of Chabannes' Daniel F. Callahan, University of Delaware Index

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.700
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.018
GPT teacher head0.199
Teacher spread0.181 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2014
Admission routes1
Has abstractyes

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