A corpus-based approach to Chinese English study——Pinning down the ‘Chineseness’ and implications for creative writing in English in China
Bibliographic record
Abstract
There has been limited research on Chinese English literature (CEL) in the domain of contact literatures. This article reports on a study of a representative Chinese English (CE) literary work well received by a worldwide audience – Qiu Xiaolong’s Enigma of China. With the aim of exploring CE by analysing the unique ‘Chineseness’ in this CE literary work from the paradigms of corpus linguistics and sociolinguistics, the language innovations and sociocultural meanings embedded in different levels of the work are examined. Further, by adopting a corpus-based approach and conducting keyword analysis, a number of language innovations were identified. These included the use of innovative hybrid compounds at the lexis level, the use of hybrid Chinese sentences of parallelism at the syntax level, and the use of discourses on political ideology and employment of ancient Chinese poems at the level of discourse pragmatics. It is argued that these language innovations are manifestations of a transfer of traditional Chinese culture norms and political ideology. The question of how to integrate CE corpus into courses on English creative writing in China is also discussed.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".