The Change of One Translator's Style and Cognition: A Speech-act Report Verb Study of Allan Barr's Two Translations of Yu Hua's Works
Bibliographic record
Abstract
This article aims to explore the changes in a translator’s style with time and translation practice. Allan Barr was selected as the research subject, considering that his five translations of famous writer Yu Hua's works lasted for 16 years. Instead of focusing on one translator's consistent stylistic features throughout all his or her translations by comparing their translations with others, this study demonstrated the shift of a translator’s style through the revised source-oriented model for the study of the style. A systematic analysis was conducted on the quantitative and qualitative data regarding the English translations for the most frequent speech-act report verbs collected from the parallel corpus. The self-established corpus comprises Barr's earlier translation of “Boy in the Twilight: Stories of the Hidden China” (Yu, 2014), which was completed in 2003. This was followed by his later translation for “The April 3rd Incident: Stories” (Yu, 2018), which was completed in 2018. Notably, Barr's understanding of style and translation ideology may change with time and translation practice, as indicated by the significant difference in the frequency of the speech-act report verbs with the same semantic meanings in ST and TT. This condition was also indicated through the distinguished motivations to change the semantic meaning of the speech-act verbs in the ST between Barr's earlier and subsequent translations. Subsequently, it was found that the method of corpus-based translator's style study can be applied to investigate how social and cognitive factors motivate a translator to change his or her translation ideology and strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".