A Study on Self-Translation of Eileen Chang’s Little Finger Up From Perspective of Translator’s Subjectivity
Bibliographic record
Abstract
Based on both English and Chinese texts, this paper, with the help of corpus software, attempts to make a detailed analysis of translator’s subjectivity as revealed in Eileen Chang’s self-translation of Little Finger Up in terms of passivity, subjective initiative and purposefulness (self-benefiting) as well. Thereupon, the paper comes to the following conclusions. First, as the self-translator, Eileen Chang brings her subjective initiate into play in the self-translation in regard to sentence structure, proper nouns, culture-specific items, manifestation of the theme and way of expressing feelings. Second, privileged as she is, Eileen Chang is affected by both ideology and poetics. She deliberately eschews the sensitively political and warlike topic by way of omission and retains the heterogeneous elements of the source culture in the process of translation, reflecting her translator’s subjectivity in the self-translation while suffering the passivity imposed by ideology and poetics. Third, Eileen Chang usually adopts various strategies in the self-translation so as to fulfill her translation purposes, in which she deliberately deletes the plots and rewrites the title so as to highlight the problem of Chinese marriage and reveal her own pessimistic attitude towards marriage, indicating her self-benefiting in self-translation. In a nutshell, the self-translation seems to be concise and comprehensive as well as natural and unrestrained, indicating that the translator’s subjectivity is much more involved in self-translation, compared with that in conventional translation.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".