Exploring the Relationship Between Translators’ Styles and Translation Competence: A Case Study of English Translations of The True Story of Ah Q
Bibliographic record
Abstract
This study investigates the relationship between Translators’ Styles and Translation Competence (TC) by examining five English translations of The True Story of Ah Q. The five translators are George Kin Leung, Chi-Chen Wang, Yang Xianyi & Gladys_Yang, William Lyell, and Julia Lovell. Employing a mixed-method approach, the research utilises the Multidimensional Analysis Tagger (MAT) for quantitative analysis and the PACTE model framework for qualitative analysis. The key competencies of TC—bilingual, extralinguistic, strategic sub-competence, and knowledge about translation—are evaluated to understand their impact on stylistic choices in the translations. MAT’s quantitative analysis provides insights into the lexical density, syntactic complexity, and narrative techniques, while the qualitative analysis explores cultural transmission and rhetorical strategies used by the translators. The findings reveal significant variations in translator style, influenced by their competencies. Translators with higher bilingual and knowledge about translation demonstrate greater linguistic flexibility and accuracy, whereas strategic and extralinguistic sub-competence impact cultural adaptations and rhetorical fidelity. This research contributes to expanding the PACTE model’s application to literary translation, offering empirical support for how TC manifests in stylistic choices. The study highlights the value of combining quantitative and qualitative approaches to advance the understanding of translator style and competence in translation 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 | 0.017 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".