Strategies Applied by Native and Non-native Translators to Transfer Persian Culture-Specific Items: A case study on an Iranian novel
Bibliographic record
Abstract
One of the most challenging tasks for all translators is how to render culture-specific items. Transferring culture-specific terms from one culture to another and understanding them by the target audience in the target culture is dependent on having familiarity with the source cultures and traditions. The present research has been conducted in order to find firstly to what extent the strategies of translating CultureSpecific-Items applied by native and non-native translators differ from each other in terms of frequency and secondly to determine the most frequent translation strategies applied by native translator compared to non-native translator in translating culture-specific items based on Aixela's categorization. The corpus used in this study was Sadeq Hedayat’s Persian novel, The Blind Owl and its two translations. Considering the definition given by Aixela (1996) for distinguishing CSIs, almost all the CSIs applied in the original book were identified and consequently their equivalents in the two translations (one by native Persian-speaking translator and the other by non-native Persian-speaking translator) were found and categorized. At first those translated incorrectly were distinguished and removed. Then according to the theoretical framework used, Aixela's (1996), CSIs translated were classified under two major groups namely conservation and substitution and then their sub-groups. In each sub-group, some CSIs translated through that strategy were described. At the end the number and percentage of CSIs translated through each strategy were provided.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".