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Record W2913630952 · doi:10.7202/1055142ar

Lolita’s Love Affair with the English Language: Heterolingualism and Voice in Translation

2018· article· en· W2913630952 on OpenAlexvenueno aff
Margarida Vale de Gato

Bibliographic record

VenueMeta Journal des traducteurs · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsHybridityFocus (optics)AmbiguityContext (archaeology)Translation studiesSociologyHistoryPhilosophyAnthropology

Abstract

fetched live from OpenAlex

This article is a first-person account of the translation of Lolita into Portuguese dealing primarily with the question of how to treat English as a source language that should be replaced by the translating language. The novel foregrounds the narrator’s stridency as a non-“native illusionist” (Nabokov 1955/1991: 317), along with a heterolingual bend, presenting remarkable challenges for translation: how to represent the geopolitics of linguistic hybridity in the TT and how to maintain the ambiguity of alignments between (implied) reader(s), author(s) and competing instances of narratorial authority, including the “fictional translator” (Klinger 2015: 16). Selective non-translation is suggested as an option for addressing linguistic hybridity through which, in this context, the “differential voice(s)” (Hermans 2007; Suchet: 2013) might foreground linguistic (and hence cultural/ideological) difference and deviation. The adherence to a strategy of “overt translation” (House 2001) is not intended to break the “translator’s pact” (Alvstad: 2014); it refuses, however, the convention of transparency as one of its tenets. It also shifts the focus from phonocentric authority to a polyphonous palimpsest and an archaeology of language(s) – not an entrenched foreignization, but an availability for “other-languagedness” (Bakhtin: 1981).

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 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 score0.972
Threshold uncertainty score0.531

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.277
Teacher spread0.227 · 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.

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

Citations3
Published2018
Admission routes1
Has abstractyes

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