Reviews on the Turns of Translation Studies and the Definitions of Translation
Bibliographic record
Abstract
With the development of such disciplines as linguistics, literature, sociology, anthropology, psychology and the rise of deconstructionism, post colonialism, feminism, more and more theories are applied to translation studies since the 1950s. The introduction of the theories from various kinds of thoughts and disciplines not only offers new perspectives for translation studies, but also brings new turns to it. As a specific turn of translation studies is one of the nuclear parts of translation studies, the study of translation and its turns attracts some scholars’ attentions. Abroad, the representative figures are Andre Lefevere, Susan Bassnett, Mary Snell-Hornby, Jeremy Munday, and Edwin Gentzler and so on. In China, there are few scholars such as Wang Ning, Lu Jun, Xie Tianzheng have ever studied on the turns in translation studies. Owing to the role translation definition plays in translation studies as well as the turns of the methodology, the aspects of the research background are viewed in this paper: the studies on the translation turns and the definitions of translation at home and abroad. Based on literature review and comparative analysis, the paper finally comes to the conclusion: although translation studies have seen a great number of turns, few scholars studied turns of translation systematically and showed interests in the influence of the translation definitions on the turns.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".