A Study of Creative Treason in Red Sorghum : From the Perspective of Rewriting Theory
Bibliographic record
Abstract
In literary translation, the translated works always deviate from and distort the original texts to a certain extent because of the linguistic and differences between nations. Therefore, Robert Escarpit，a French literature sociologist, puts forward the notion of “creative treason”. He claims that “translation is always a kind of creative treason”. Translated by Goldblatt, Red Sorghum , the English version of Hong Gao Liang Jia Zu which is written by Mo Yan and a representative of contemporary Chinese novels, is no exception. However, the traditional translation approach cannot give a full explanation to the “creative treason” in Goldblatt’s translation, let alone judge the translator and his translated work objectively and fairly. Since translation studies took the cultural turn in 1970s, some translation theorists have adopted a descriptive method to analyze translation from the socio-cultural perspective. During this process, Lefevere, the leading figure of Manipulation School, deserves special attention. The Rewriting Theory proposed by him expanded the horizon of translation studies. Taking this thoery as theoretical foundation, this paper aims to analyze the underlying causes for Goldblatt’s “creative treason” in Red Sorghum and explore a new route in research of Goldblatt and his translations. Meanwhile, the author hopes that this paper can bring some suggestions to the transmission of contemporary Chinese literature abroad.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.005 | 0.006 |
| 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".