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Record W2001224836 · doi:10.7202/037243ar

Littérature et diglossie : créer une langue métisse ou la « chamoisification » du français dans Texaco de Patrick Chamoiseau

2007· article· fr· W2001224836 on OpenAlexvenueno aff
Marie‐José Nzengou‐Tayo

Bibliographic record

VenueTTR traduction terminologie rédaction · 2007
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

Littérature et Diglossie : créer une langue métisse ou la « chamoisification » du français dans Texaco de Patrick Chamoiseau — Le succès de Texaco et les commentaires élogieux concernant l'usage du français et du créole par l'auteur invitent à examiner de plus près le jeu linguistique à l'oeuvre dans le roman en prenant en compte les positions théoriques avancées par Bernabé, Chamoiseau et Confiant dans Éloge de la créolité (1989). Bien que le roman s'inscrive dans une tradition de l'utilisation littéraire du créole fort ancienne, il est possible d'identifier la spécificité de l'écriture de Chamoiseau. Pratiquant une poétique de « l'inquiétante étrangeté », il crée un « effet-de-créole » grâce auquel il déroute et séduit ses lecteurs hexagonaux aussi bien qu'antillais. Placée sous le signe du Baroque, cette écriture est devenue le symbole du métissage culturel et/ou du processus de créolisation, caractéristique des cultures de la région des Caraïbes et riche de la promesse d'un nouveau rapport-au-monde basé sur le plurilinguisme.

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)0.000
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 score0.173
Threshold uncertainty score0.344

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0070.003
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.001

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 head0.038
GPT teacher head0.300
Teacher spread0.263 · 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

Citations12
Published2007
Admission routes1
Has abstractyes

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Same venueTTR traduction terminologie rédactionSame topicLinguistics and Discourse AnalysisFrench-language works237,207