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Record W2977466794 · doi:10.29228/ijla.38223

DIFFICULTIES AND CHALLENGES OF LITERARY TRANSLATION THE TRANSLATION OF SOME CULTURAL ELEMENTS IN THE STORY OF "ARABIC AS A SECRET SONG" BY NOVELIST LILY SPAR AS A MODEL

2019· article· en· W2977466794 on OpenAlexaff
Saber Oubiri

Bibliographic record

VenueInternational Journal of Language Academy · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicTranslation Studies and Practices
Canadian institutionsConcordia University
Fundersnot available
KeywordsArabicLiterary translationSparLiteratureTranslation (biology)LinguisticsArtHistoryPhilosophyEngineeringGenetics

Abstract

fetched live from OpenAlex

Catford defines translation by saying, "It is a process of languages where one text in one language is replaced by text in another" (our translation).In general, we can distinguish between two types of translation: scientific translation and literary translation.Both types are problematic, but the translation of the literary text raises distinct problems because the literary text itself has characteristics that distinguish it from other texts, including, but not limited to: the control of expressive function, suggestive power, multiple meanings and plurality of interpretation (Muhammad Jaber, 2005).This paper examines the difficulties and challenges of literary translation.The study deals with the translation of some elements of culture by analyzing the translation of the story of the novelist Lili Spar, translated from French into English entitled: L'Arabe comme un chant secret.We also study the methodology of translation or translation strategy adopted by the translator Skyler Artes (Skyler Artes) to overcome the difficulty of translation of literary text charged with cultural elements.In his book, Translation, Rewriting and the Manipulation of Literary Fame, André Lefevere provided an accurate analysis of the cultural and social factors that dominate the translation process.He stated that it was the ideology and literary style found in the original text when translating.We seek to identify the difficulties and challenges faced by the translator when confronting the translation of literary texts and the means that can be followed to overcome such difficulties by asking the following questions: What are the characteristics of the literary text and what are the difficulties of literary translation?What translation strategy or theory can the translator follow in order to overcome these difficulties?What are the conditions to be met in the literature translator?

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)0.081
metaresearch head score (Gemma)0.160
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 score0.081
Threshold uncertainty score0.430

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0810.160
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.007
Science and technology studies0.0170.062
Scholarly communication0.0250.026
Open science0.0050.014
Research integrity0.0100.011
Insufficient payload (model declined to judge)0.0030.002

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 head0.057
GPT teacher head0.316
Teacher spread0.258 · 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

Citations0
Published2019
Admission routes1
Has abstractyes

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