Pre-Translating Process in Literary Text
Bibliographic record
Abstract
Literary translation represents a crucial sub-field of Translation Studies. This sub-category has inadvertently spurred researchers and academics to unleash practical and theoretical controversies. It involves transferring canonical literary genres into different languages and cultures. While the genres themselves may not stem from similar experiences or similar perceptions of the world, they share the same concern of addressing cultural, historical, philosophical, and religious innermost thoughts of the people and countries they represent. Thus, if these various genres are not properly repainted in a different language and culture, many of the finest pieces of literature produced by well-known writers will remain beyond reach and/or lost. This paper attempts a translation assessment of the Arabic oriental mythology of Sindbad al-Rahhal at the extra-textual level translates into English as Sindbad and the Sailor. It, therefore, applies Nord’s Model (2005) and compares the Arabic version of the story with its English counterpart as traversed from the East to the West by Malcolm C. Lyons (2008) in order to examine the extra-textual factors of the ST and TT. Eventually, this will invite discussion in this paper on issues such as background knowledge, literary expertise, and cultural knowledge as essential factors for translators before they embark on their mission. The results of the current study reveal that Nord’s Model (2005) is applicable to the ST and TT.
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 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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.008 |
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".