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Record W4410724531 · doi:10.1111/rest.12994

Print Conventions and Authority in Three English Recipe Manuscripts

2025· article· en· W4410724531 on OpenAlexaff
Aylin Malcolm, Margaret C. Maurer

Bibliographic record

VenueRenaissance Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics and language evolution
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsRecipeHistoryComputer scienceArtAncient history

Abstract

fetched live from OpenAlex

Abstract This article considers the uses of stylistic and visual conventions drawn from print books in three seventeenth‐ and eighteenth‐century recipe manuscripts at the University of Pennsylvania. We begin by analysing the title page, dedicatory epistle, catchwords, and headers of MS Codex 627, which imitates an edition of Hugh Plat's Delights for Ladies without reproducing the contents of this popular print text. These features evoke the authority of print while retaining the flexibility and personal character of manuscript recipe books. We then consider the title of MS Codex 251, which synthesizes print title conventions to emphasize the novelty, volume, and variety of its contents. Its creator, Mary Statham, also claims altruistic motives in a manner common among print texts. Our third case study, MS Codex 625, is a copy of Edward Kidder's culinary recipes with a print title page, which we compare to Kidder's engraved edition of these recipes. Both texts combine print and manuscript conventions to enhance Kidder's authority, aligning his recipes with the valued familial knowledge of domestic manuscripts while emphasizing his importance as sole author. Finally, we address the continuing transformations of conventions across media and their relationships to authority via a twentieth‐century edition of Kidder's Receipts .

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)0.004
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0080.009
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.060
GPT teacher head0.301
Teacher spread0.240 · 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 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
Published2025
Admission routes1
Has abstractyes

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