The Pattern and Translation of Chinese Address Terms in Contemporary Film Happiness Around the Corner
Bibliographic record
Abstract
This paper discusses the translation and classification of Chinese address terms by selecting a representative film with a range of contexts. The data for this study were collected from a Chinese comedy film Happiness Around the Corner screened in 2018 with a length of 91.34 minutes. This film was chosen firstly because it was highly rated by the Chinese mainstream media People’s Daily Overseas Edition for its conveying of positive energy in a humorous manner. Furthermore, considering a range of contexts and interlocutors in this film, the data collected from this film would be sufficient to reach the expected goals of the study. This study employs a qualitative approach to explore a proper classification of Chinese contemporary address terms. The Chinese address terms found in the selected film were classified into seven types: namely nickname, professional title, kinship terms, fictive kinship terms, professional title with surname, offensive address terms and full name. The findings show that professional title, nickname and kinship terms appeared with higher frequency than offensive address terms and full name in this film, which could be explained from the perspective of sociolinguistics. Literal plus liberal translation strategies are recommended to translate address terms using homophonic puns, and equivalent words in target language are advised in most cases. These findings could not only throw light on the classification of address terms in Chinese, but also promote translation studies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".