Effect of Previous Translations on Retranslation: A Case Study of Russian-Dutch Literary Translation
Bibliographic record
Abstract
As Outi Paloposki and Kaisa Koskinen (2010) correctly stated in their article on the “fine line between retranslating and revising”, the exact relationship of a text with the previous translation(s) cannot always be determined, even if the most recent translation is presented (in the paratext to the edition) as a ‘retranslation’, ‘re-edition’ or ‘revision’. Indeed, in practice the “labels” of “(re)translation, (…) revisions, adaptations and retellings” are “hard to separate and cover different contents” in different circumstances (Van Coillie 2014). In this paper I will try to study the effect of a first or previous translation on the process of retranslating. In order to do that I will compare a number of literary translations with their predecessors. Literary translations are specifically chosen here because they are probably more often retranslated than other types of texts. Moreover, the style of the translated text is presumably more important than for other genres, which allows me to mutually compare the translations not only at lexical and syntactic, but also at stylistic level. In order to visualize the effect of a previous translation on a retranslation I will compare a number of Dutch translations of Russian literary works with their retranslations. On the one hand, I will compare three recent retranslations with older translations of the same work, ordered and published by the same publishing house, and explicitly announced to the reader as a ‘refreshed’ and reworked translation of the previous one. In these particular cases the retranslators were fully aware of the existence of another translation and the explicit reference to ‘retranslation’ virtually forced them to use the previous translation as a starting point. In order to avoid the influence of (1) the changing translation strategies throughout time, and (2) the possible idiosyncratic peculiarities of the specific translation strategy of one particular (but not representative) translator, I will use translations made by three different translators and published in approximately the same period. On the other hand, I will investigate two Dutch translations of the same Russian literary work, that were made independently from each other and were published virtually on the same day, as the translators were unaware of each other’s translation effort. In this case the ‘previous’ (the term is not really applicable in this case) translation could not have had any effect on the ‘retranslation’, which makes it an interesting case to compare with the three genuine ‘retranslations’. Special attention will be drawn to the differences in the translator’s decisions at lexical, syntactical and stylistic level. More specifically, the analysis will include a quantitative and qualitative approach. I will establish the amount of overlap in lexical, syntactical and stylistic choices in the four pairs of texts, and will try to find whether the retention or substitution of certain terms and linguistic features can be explained.
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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 | 0.016 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".