Subjectivity Identification: A Case Study of Chinese-to-English Translation of Hakka Proverbs
Bibliographic record
Abstract
This paper proposes some strategies for translating Hakka proverbs from Chinese into English. Unlike the previous scholarship, this research emphasizes the identification of the subjectivity of source culture and the subjectivity of translator by investigating the English translations of eighty Hakka proverbs, randomly collected from Hakka websites, using source-oriented strategies and annotations. Drawing on L. Venuti’s (1995) foreignization translation and K. A. Appiah’s (1993) thick translation as the theoretical framework, this research discussed how proverb translation could send the audience toward the source culture and how in-text annotations rendered the translation contextually thicker and revealed the translator’s opinions. The results of investigation showed that in order for source cultural attributes to be vividly replicated, some strategies can be used including: 1) literal translation with grammatical modification and 2) literal translation with syntactic, grammatical and lexical modifications. In addition, for the translator to unveil his/her subjectivity, the strategies can be: 1) supplementing explanations and 2) adding a commentary note to the literal translation. The strategies together help proverb translation take on the new significance of dual subjectivity by demonstrating the true identity of the source culture on the one hand and exposing the translator’s own voice on the other hand.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".