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Record W1567306975 · doi:10.7202/008557ar

Translation and the Authorial Image: the Case of Federico García Lorca’s Romancero gitano

2004· article· en· W1567306975 on OpenAlexvenueno aff
Stella Linn

Bibliographic record

VenueTTR traduction terminologie rédaction · 2004
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsnot available
Fundersnot available
KeywordsGarciaMythologyLiteratureRomanceCriticismArtPersonaLiterary criticismLiterary translationHistoryArt historyHumanities

Abstract

fetched live from OpenAlex

Despite Barthes’s claim that the author is dead, leaving the scene for his work, freed from its all too personal origin, I would like to argue that the author image is far from absent in the practice of literary translation. On the one hand, the author’s image within a particular literary and social system may determine which work is translated, and even how it is translated. On the other hand, it seems likely that some characteristics of a persona will be highlighted more than others, depending on which source texts are selected for translation and on how the author and his or her works are presented in prefaces and commentaries accompanying the translations. Moreover, the translation strategy may enhance the prevailing tendencies within reception and thus contribute to a certain perception of the author in the target culture. In this paper I will investigate these hypothetical connections, taking as an example the Spanish author Federico García Lorca and a number of translations of his Romancero gitano (1928) into French, English, and Dutch. I will examine a possible correlation between the prevailing “folkloristic” image of Lorca in the early literary criticism, and the emphasis on romantic, naïve and mythological aspects in translations of his work, and conversely, the later, more complex and gloomy image presented of the author, and translation strategies which highlight elements that correspond to that view.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.829
Threshold uncertainty score0.798

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
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 head0.095
GPT teacher head0.301
Teacher spread0.207 · 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 designOther design
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

Citations6
Published2004
Admission routes1
Has abstractyes

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