Translations of Sovietisms: A Comparative Case Study of English Translations of Bulgakov’s The Master and Margarita
Bibliographic record
Abstract
Bulgakov’s novelThe Master and Margarita(1966-1967), a highly complex and multi-levelled narrative, is a challenge for any translator. Because Bulgakov’s narrative has been translated into English seven times (twice by the same translator, Glenny), with the first two translations published in the same year, 1967, and the latest in 2008, the novel offers a unique insight into the analysis of translation shifts, not merely from a synchronic, but also from a diachronic perspective. The emphasis here is on the translation of historicalrealia, referred to asSovietisms, and pertaining to items characteristic of Soviet discourse of the 1930s, word-formations of the non-standard “Soviet Russian.” Bulgakov’s language is sated with Soviet vocabulary which refers to various cultural and socio-political elements of Soviet reality.Sovietismsoccur at various levels (lexical, syntactical, stylistic and rhetorical) and should be carefully translated as a significant characteristic of Bulgakov’s style. A complete domestication ofSovietismsmay lead to a loss of a connotative meaning essential for understanding the context, while a foreignization of these terms which are most likely unknown to Western readers may disturb the fluency of reading. The purpose of the analysis, thus, is to illustrate the use of domesticating/foreignizing strategies employed by the translators and to assess the translation choices, considering that the target audience of English-speaking readers are most likely completely unfamiliar with most terms. The analysis employs theory on foreignizing and domesticating principles, as well as taxonomies suggested by Vinay and Darbelnet (1958/1989), Vlakhov and Florin (1980) and Aixelá (1996) as the grounds for the case study.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".