Translation of Hollywood film titles: Implications of Culture-Specific Items in Greater China
Bibliographic record
Abstract
In view of the lack of updated analysis on film title translation in Greater China, the present study attempted to investigate translation of culture-specific items in Hollywood film titles among three regions of Greater China: Mainland China, Hong Kong and Taiwan. From 1989 to 2018, a film title database was built, comprising of 2472 source texts and over 7410 target texts. Culture-specific items were identified and classified into five themes, namely toponym; anthroponym and fictional character; forms of entertainment; means of transportation; and social taboos. Analysis was in two tiers: First, translation methods under each theme was compared within target regions. Second, corresponding cultural implications of the three target regions were discussed using the concept of glocalisation. In a translational perspective, adaptation was highly favoured by Hong Kong under film title translation, whereas transliterations and literal translations were preferred by Mainland China. In a cultural perspective, both Mainland China and Hong Kong were found to preserve local cultures via translation. While Mainland China attempted to protect the purity of Chinese language through using transliterations and literal translations, Hong Kong used Cantonese slangs and jargons to replace culture-specific items in source text. Different from the former regions, Taiwan adopted exotic and explicit translation of social taboos. The present research sheds new light on Translation Studies research by analyzing film title translation in a sociocultural perspective, and thus can offer stakeholders in the film industry to appreciate translation in another perspective.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".