Bibliographic record
Abstract
This paper examines the social aspects of retranslation in contemporary Iran. Foreign classics and award-winning literary books have attracted multiple translations into Persian within a short period of time. For instance, George Orwell’s novella, Animal Farm, has received more than one hundred retranslations in the last 40 years. The aim of this paper is to investigate possible reasons for such an unusually high number of retranslations. By analysing sixteen interviews with Iranian translators and publishers and performing paratextual analysis of four retranslations of George Orwell’s Animal Farm, this paper sheds light on the perceived advantages and disadvantages of retranslation. It does so by drawing on the trust-based approach to the study of translation proposed by Rizzi, Lang, and Pym, and by offering sociological insight into retranslation in contemporary Iran. Four groups of translators are identified: amateur, early career, mid-career, and senior translators. Retranslation for the former two groups is viewed as profitable trade in literary translation market. For the latter two, retranslation is the process of reinforcing trustworthiness at the institutional level that means trust in professionalism of certain Iranian translators and publishers.
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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 | 0.018 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.018 | 0.044 |
| Scholarly communication | 0.008 | 0.014 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".