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Preface, Prescription, and Principle: The Early Development of Vernacular Emblem Proto-theory in France

2017· article· en· W2765343519 on OpenAlexaff
David Graham

Bibliographic record

VenueJANUS Estudios sobre el Siglo de Oro · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsConcordia University
FundersAustralian and New Zealand Association for Medieval and Early Modern StudiesUniversity of Glasgow
KeywordsVernacularEmblemHumanitiesPhilosophyArtLinguisticsLiterature

Abstract

fetched live from OpenAlex

The first French-language treatises on emblems appeared in the seventeenth century, but prior to their appearance, many authors of emblem texts and vernacular translations offered glimpses in their paratextual material—prefaces and dedicatory letters, for the most part—of their concept of the emblem. As a rapidly emerging genre, the emblem itself underwent a series of transformations, and and attempt at developing a true theory of the emblem was rendered impossible not only by those but by the commercial pressures associated with attempting to take advantage of the new market for illustrated books of all kinds. This essay examines the progress of thinking about the emblem in France from the earliest times to the period immediately prior to the publication of Claude-François Ménestrier’s Art des emblemes in 1662, and explores some of the reasons why the birth of emblem theory in French was so long delayed.

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)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.047
Threshold uncertainty score0.094

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0070.016
Scholarly communication0.0080.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.249
Teacher spread0.228 · 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 designTheoretical or conceptual
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

Citations3
Published2017
Admission routes1
Has abstractyes

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