The Study of English Culture-Specific Items in Persian Translation Based on House’s Model: The Case of Waiting for Godot
Bibliographic record
Abstract
Each society’s language differs from other societies’ languages; because of the distinctions over the dialects, implications and ideas fluctuate crosswise over two unique dialects. The clearest purposes of distinction between dialects show up in their writing, which contains a lot of culture-specific items (CSIs); this causes some degree of complexities while exchanging implications and ideas from a languege into another. The present study was an attempt to discover proposed interpretation techniques connected in the two translations of Waiting for Godot by Aliakbar Alizad (1385, 2006) and Asgar Rastgar (1393, 2014). There are great differences in the translation. The practice of translators in rendering cultural items of the original reflects their different attitude in the choice of translation strategies The hypothetical structure of this examination depended on the cultural items classification and strategies proposed by Newmark (1988) as well as Houses’s (1997) model of translation quality assessment. After extracting cultural items of the original text and their classification, each item was compared and contrasted whit its corresponding rendering in the two translations. The strategies used by translators were then determined. The findings showed that Alizad’s work is an overt translation, while Rastgar’s work is a covert one. Rastgar’s strategies led into great differences with the original. His overuse of informal words and expressions as well as cultural items has domesticated his translation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".