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Record W4320893572 · doi:10.7202/1096699ar

Les chants des oiseaux dans les fables : topoï, types et savoirs zoologiques

2023· article· fr· W4320893572 on OpenAlexvenueno aff
Élodie Ripoll

Bibliographic record

VenueTopiques études satoriennes · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicHistorical and Literary Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBestiaryTopos theoryNarrativeCharacter (mathematics)ArtLiteratureHumanities

Abstract

fetched live from OpenAlex

Le bestiaire très riche des fables de la fin du XVIIe siècle offre un terrain d’exploration idéal pour vérifier l’hypothèse des topiques sonores. Parmi les nombreux corbeaux, cigognes, coqs, hiboux, pigeons, moineaux, aigles, paons, rossignols, tous ou presque ont une voix singulière, reconnaissable, imitable. Tous ou presque sont anthropomorphisés, parlent et jouent un rôle essentiel dans la fiction. Cet article cherche ainsi à répondre aux questions suivantes à partir des fables de La Fontaine, Perrault, Furetière, Fénelon et Mme de Villedieu ainsi que de Philippe Desprez et Houdar de la Motte : leschants des oiseaux sont-ils toujours envisagés selon des critères esthétiques ou selon leurs effets sur l’auditoire ? Peut-on dégager des topoï narratifs associés aux chants des oiseaux, voire aux chants de certains oiseaux ? Ou les oiseaux sont-ils plutôt des types dont le chant spécifique serait une caractéristique ? Quelle est la place des savoirs zoologiques dans ces représentations ?

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

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 studies0.0040.007
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.088
GPT teacher head0.336
Teacher spread0.249 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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