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Record W2513484440 · doi:10.7202/1037124ar

Baudelaire, l’opium et la poésie : digestion, digression et adaptation

2016· article· fr· W2513484440 on OpenAlexvenueno aff
Camille Muris-Prime

Bibliographic record

VenueTTR traduction terminologie rédaction · 2016
Typearticle
Languagefr
FieldArts and Humanities
TopicFrench Literature and Poetry
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

Baudelaire n’est pas que le traducteur de Poe. Il a aussi adapté Confessions of an English Opium Eater de Thomas De Quincey. Devenu Un mangeur d’opium et placé en deuxième partie des Paradis artificiels , ce texte est utilisé par Baudelaire de plusieurs façons. Le poète s’attache à remanier le texte de sorte à faire du travail de traduction un véritable travail d’adaptation, et il le place aussi au sein d’un diptyque afin de l’utiliser dans sa démonstration sur les drogues. L’analyse du texte de Baudelaire permet de comprendre la façon dont se développe son projet esthétique qui consiste à comparer l’état dans lequel met la drogue au paradis poétique auquel tente d’accéder le poète. La traduction permet aussi à Baudelaire de préciser sa position éthique puisqu’il réussit à adapter l’oeuvre de De Quincey tout en la laissant visible, la rendant lisible tout en dialoguant avec elle. Si Un mangeur d’opium est un texte assez peu connu, il est pourtant un laboratoire où la traduction permet à Baudelaire de mettre en abyme sa propre pratique poétique.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.933
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.291
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 teacher head, not a consensus.

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

Citations0
Published2016
Admission routes1
Has abstractyes

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