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Record W1573200506 · doi:10.7202/011859ar

Écrire-traduire entre les langues

2005· article· fr· W1573200506 on OpenAlexaffvenue
Gillan Lane-Mercier

Bibliographic record

VenueVoix et Images · 2005
Typearticle
Languagefr
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesArtTrope (literature)PhilosophyLiterature

Abstract

fetched live from OpenAlex

Cet article propose, dans une perspective traductologique, une réflexion sur la traduction et le bilinguisme comme effets d’écriture dans les romans de Gail Scott. Définis comme expérience quotidienne, comme pratique auto-réflexive et comme trope, les effets de traduction et de bilinguisme sont tributaires à la fois de l’anglais « minorisé » employé par Scott et de la présence de fragments — voire de passages entiers — rédigés en français. Il s’agit d’examiner les modes d’inscription de ces effets, les images/valeurs qu’ils véhiculent, ainsi que les postures esthétiques, idéologiques et éthiques qu’ils présupposent face à l’altérité linguistique et culturelle.

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)0.006
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: none
Teacher disagreement score0.018
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0060.027
Scholarly communication0.0060.005
Open science0.0010.005
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0080.001

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 head0.037
GPT teacher head0.293
Teacher spread0.256 · 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

Citations8
Published2005
Admission routes2
Has abstractyes

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