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Record W4391312407 · doi:10.33137/rr.v42i1.32855

« Treschevaleureux capitaines » contre « crocheteurs de flascons » : lecteurs et lectorats dans les Fascetieux devitz des Cent nouvelles nouvelles du Seigneur de La Motte Roullant

2019· article· fr· W4391312407 on OpenAlexvenueno aff
Nora Viet

Bibliographic record

VenueRenaissance and Reformation · 2019
Typearticle
Languagefr
FieldArts and Humanities
TopicHistorical Studies and Socio-cultural Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsArtHumanities

Abstract

fetched live from OpenAlex

Le récit bref de la Renaissance doit en partie son succès à son ubiquité sociale : fables, fabliaux, nouvelles circulaient dans les sphères les plus diverses, de la cour aux cercles humanistes, des milieux bourgeois au clergé. Avec la vogue du recueil de nouvelles dans les années 1540-1550, on voit pourtant s’afficher, dans les péritextes, des figures de lecteurs littérairement et socialement marquées, parfois explicitement opposées entre elles : lecteurs bourgeois et lecteurs de cour, honnêtes dames et gentilshommes, amateurs de nouvelles à l’italienne et lecteurs de contes gaulois. L’article se propose d’analyser ces clivages à partir d’un recueil où ils s’expriment avec un relief particulier : les Fascetieux devitz des Cent nouvelles nouvelles, imprimés à Lyon sous le nom d’auteur de La Motte Roullant (1549). La représentation d’un lectorat différencié et haut en couleur participe ici de l’élection d’un public de nouvelles à la française.

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)0.001
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: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.005
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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