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
Bibliographic record
Abstract
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?
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.081 | 0.160 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.017 | 0.062 |
| Scholarly communication | 0.025 | 0.026 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".