Analysis of Culture-Specific Items and Translation Strategies Applied in Translating Jalal Al-Ahmad’s by the Pen
Bibliographic record
Abstract
<p>Due to differences across languages, meanings and concepts vary across different languages, too. The most obvious points of difference between languages appear in their literature and their culture-specific items (CSIs), which lead to complexities when transferring meanings and concepts from one language into another. To overcome the complexities arisen from the distinction between languages in the process of translation, translation scholars have proposed different strategies. Newmark’s proposed taxonomy for translating CSIs is the framework for achieving this study. So, after adopting CSIs with Newmark’s (1988) 5 proposed domains of CSIs, we sought to find his proposed translation strategies applied in the English translation of Jalal Al-Ahmad’s <em>By the Pen</em> by Ghanoonparvar (1988) and to evaluate the frequency of each in order to determine which strategy could help the most in translating CSIs. To do so, first, both the source language text and its translation were studied; then, the translation strategies applied were found. Having found the strategies as the sources of the data, they were arranged and analyzed. Results showed that functional equivalent was the most frequently used strategy, and modulation and paraphrase were the least frequently used ones. Findings have pedagogical implications for translation students and literary translators.</p>
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".