Translation as Accommodation: Influences of Contextual Factors on a Translator
Bibliographic record
Abstract
The present study attempts to investigate how contextual factors with ideology as the major component affect the process of translation and influence translators to adopt accommodations in translated text because all translations are ideological in one way or the other because preference of definite terms is motivated by the aims and interests of target society. For this purpose the researcher selected short story ‘Overcoat’ written by renowned Pakistani writer Ghulam Abbas and its English version included in syllabus of intermediate class. The author is a professional Urdu writer whose text is translated by PTB Lahore for academic purpose. The translated text was analyzed and compared and contrasted with the original text to trace the accommodation patterns which are adjustments to adjust target text in new context. The source context is informal, free and wide with a variety of readerships whereas target context is formal, restricted and with a specific type of readerships. Analytical framework is a fusion of Van Dijk (2005) approach of text analysis and Shi’s (2004) model of translation as accommodation. The analytical framework demonstrates that the analysis of the texts starts individually and then after analyzing macro and micro structures of the texts comparatively, draws the conclusion. The findings show that there are significant ideological and cultural accommodations in the target text.
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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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".