Foreignization in News Translation: Metaphors in Russian Translation on the News Translation Website InoSMI
Bibliographic record
Abstract
Journalistic texts, as a rule, contain a considerable number of metaphorically used expressions. This paper investigates the handling of metaphors in Russian translations of journalistic texts in order to reveal the different translation strategies used by the translators. The research is conducted in three consecutive steps. First, we identify all metaphors in a twofold corpus of 60 original Dutch, English and Finnish newspaper articles on the one hand, and their corresponding 60 translations into Russian on the other. Secondly, we compare the use of metaphors in the translations with their source texts in order to establish the translation strategies and to determine to which extent the metaphorical expressions in the target texts display a higher degree of foreignness than those used in the source texts. Finally, we analyze the cases of foreignization in the target texts in order to find an explanation for the use of this translation strategy. The investigation shows how foreignization is adopted by the translators in a certain number of specific contexts, making the Western discourse on Russian subjects more visible to the reader, especially in these cases where the source text contains metaphors that suggest a critical interpretation of the Russian state, society or the leaders of the country.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".