Bibliographic record
Abstract
In response to an age of turmoil and oppression, Lu Xun’s fiction features irony and it is manifested in artistic form which should be retained in English translation to achieve equivalence. Failure to do so would weaken or lose the ironical effect intended by Lu Xun and result in Western readers’ inclination to neglect historical and social contexts of his time and to miss the thematic significance of his works. In view of inadequate research in this, the thesis explores the artistic form of ironical style in Lu Xun’s fiction, the preservation of form and ironical effect in William A. Lyell’s, the YANGs’ and Julia Lovell’s English translations and their strategies of compensation for inevitable loss due to cultural and linguistic differences between Chinese and English. However, over-compensation is to be avoided, for it would spoil the delicacy of irony, and so is under-compensation which would reduce the artistic value of the form of irony and cut the ironical effect. Sometimes even if compensation is applied, the ironical effect could hardly be kept intact. Behind Lu Xun’s ironical style is his concern for the future of China and the Chinese people, his indignation against oppression, his disappointment at some people’s numbness and stupidity and his sorrow toward the failure of the Xinhai Revolution. Translators should bear this in mind when rendering his ironical style from Chinese into English, otherwise they will miss the thematic significance of Lu Xun’s fiction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".