Translation as Rewriting: A Descriptive Study of Wang Jizhen’s Two Adapted Translations of Hongloumeng
Bibliographic record
Abstract
Applying Andre Lefevere’s theory of rewriting to a descriptive study of the two adapted translations of Hongloumeng by Wang Jizhen published respectively in 1929 and 1958, this paper attempts to investigate the effects the dominant ideology and poetics in a given society at a given time have on the translator’s choice of strategies in the translation process. A diachronic study of the two adapted versions of Hongloumeng as rewritings shows that most of the time the translator has to submissively adapt to the ideological and poetical power structures at different periods of time, yet it is possible for the translator to actively subvert the constraints. However, a comparison of Wang’s translations with the two complete versions of Hongloumeng indicates that ideologically a translation is first, if not foremost constrained by the dominant ideology of the society where it is initiated and published before it is read, rather than that of the receiving society only. Moreover, when poetical factors are involved, the influence from a source culture where the original enjoys a prestigious status often cannot be ignored. Wang’s rewritings of Hongloumeng also confirm the possibility for translators to go against the conditioning factors, although not so much on the ideological level as on the poetical level.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".