Shifts of agency in translation: a case study of the Chinese translation of Wild Swans
Bibliographic record
Abstract
This paper calls for a reconsideration of transitivity as a useful analytical tool in descriptive translation studies, especially for the investigation of the translation of agency. Transitivity is the ensemble of lexico-grammatical resources that “represent reality in language” (Eggins 2001/2005: 206). Such choices in translation have been usefully explored by a number of translation scholars with a strong orientation towards Systemic Functional Linguistics (SFL), although there has not been much recent scholarly interest within the wider community of translation studies (TS), perhaps due to the perceived complexity of SFL’s analytical framework. This paper returns to transitivity analysis for its continued relevance to TS, especially for those interested in uncovering the way agency, ideology, characterization and narrative are manifested in text. This paper incorporates new linguistic methodologies of analysing transitivity in combination with additional linguistic systems of voice, theme, and modality because this combination allows for a more holistic view of transitivity. Focusing on the translation of Mao’s agency at the beginning of the Cultural Revolution in Chang’s politically volatile autobiography,Wild Swans, it has found dramatic shifts in the characterization of Mao in the Chinese translation by Pu Zhang, published in Taiwan, which alters the overall narrative. This paper proposes a categorization of equivalences and shifts of linguistic agency based on a text-based analysis, thus contributing to a more systematic categorisation of the translation of agency.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".