Interpretative Translation Theory and Its Evaluation by Russian and Foreign Translators and Translation Studies Scholars
Bibliographic record
Abstract
This article analyzes the current status of the interpretative translation theory, which, compared to other translation theories, does not study the result of translation, with its dependence on multiple factors, but instead focuses on the process of translation, which does not differ depending on the language and remains the same for all types of translation and text types.The authors draw attention to the evaluation of this theory by various translation schools: French school, where the theory is universally acknowledged and accepted, taking into account the fact that this school is represented by E.S.I.T. (High School of Interpretation and Translation, Paris, France) graduates; former French colonies, where the French language has lost its influence but remains demanded in science and education (for instance, Vietnam), Canadian (English-speaking) and Russian translation schools.This work outlines the ambiguous attitude to the interpretative translation theory by many leading Russian scholars; certain discrepancies in its understanding by Canadian translation studies specialists, who pay more attention to translation issues and partially depart from the main principles of the interpretative theory.Besides, it studies the works of researchers from other countries, who have written their articles in English.The article analyzes both theoretical approaches and attitude to the interpretative translation theory of practicing translators and interpreters and provides their evaluation of this theory as a regularly applied translation technology.
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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 | 0.157 | 0.172 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".