Russian Literature Translations in Cross-cultural Research in Academic Publications of the First Quarter of the XXI century
Bibliographic record
Abstract
The article contains a review of publications of the first quarter of the 21st century devoted to the problems of translating ussian literature into foreign languages. The sections of the work provide information on theissues stated above and outline the directions of translation activities. Using examples of prose, drama, and poetic works analyzed by translation theorists and practitioners, the author outlines some features of modern reception and existence of ussian translated literature. The value of translations performedin the process of long-term creative and educational projects, with the participation of translators from several countries, in the context of preparing and conducting international conferences, theater, opera, film festivals and master classes is emphasized. The translation activities of foreign performers of ussian song classics are examined, the role of publishing houses in the development of cross-cultural research aimed at preserving and studying ussian literature as part of the world spiritual heritage is assessed. The articleis addressed to specialists in the field of translation history, literary studies, linguistic didactics, and cultural studies.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.016 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".