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Record W1994594078 · doi:10.1093/library/9.3.306

‘A New Kind of Printing’: Cutting and Pasting a Book for a King at Little Gidding

2008· article· en· W1994594078 on OpenAlexaff
Paul Dyck

Bibliographic record

VenueThe Library · 2008
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsArt historyArtLibrary scienceVolume (thermodynamics)HistoryEngineeringComputer science

Abstract

fetched live from OpenAlex

Abstract This article describes the harmonized gospel made by the Ferrar family at Little Gidding for King Charles I, c. 1635. This book, one of several of its kind, was made by cutting out printed materials and assembling the pieces into a single, illustrated narrative of the four gospels. The king's book is particularly complex, employing three complementary methods of displaying the harmonized gospel accounts. As with the Ferrars’ other gospel books, it uses images not only of gospel scenes but also of typologically-related Old Testament scenes. The article pays particular attention to the textual materials used in the book. First, it identifies the particular editions used, which include a Cambridge gospel harmony and, surprisingly, an Edinburgh New Testament printed by Robert Young in 1633, the year of Charles's Scottish coronation, suggesting that a courtier or the king himself may have played a part in supplying that edition to the Ferrars. Second, it relates the layout and apparatus of these editions to those of the Little Gidding books, noting that while the Ferrars dismantled one layout to make a new one, their books in important ways take up the textual work of the original editions while adding the meditative affect (and political danger) of Roman Catholic images.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.006
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.065
GPT teacher head0.205
Teacher spread0.140 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2008
Admission routes1
Has abstractyes

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