Translating Culture-specific Elements in Names in Hong Lou Meng: A Comparison of The Story of the Stone and The Dream of Red Mansion
Bibliographic record
Abstract
The names of the characters in Hong Lou Meng, crafted to indicate both the development of the storyline and the characteristics of the characters, contain abundant culture-specific elements, which make them difficult to render into English. On the basis of a systematic study on the original work and its two unabridged translations of Hong Lou Meng, the culture-specific elements in the original names are categorized into the following three: Chinese naming, Chinese family hierarchy and Chinese Character. Three strategies on compensation for the lost culture-specific elements are found to be employed at different levels in the two translations, including on-line compensation, off-line compensation and zero compensation. The strategy of on-line compensation involves semantic translation plus transliteration and annotation in text. Off-line compensation includes note of Chinese spelling, annotation out of text, interpretation of characters’ names in introduction, note ofcharacters’ names in appendix, introduction of characters in appendix and dendrogram of genealogy; Zero compensation includes transliteration, omission, transfer, substitution and literal translation.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".