Translation of Film Titles: A Pragmatic and Socio-Cultural Adaptation Perspective
Bibliographic record
Abstract
Film titles serve as the initial point of contact for audiences and play a pivotal role in how films are promoted and to what extent they succeed. They carry distinct cultural meanings and linguistic characteristics that capture the film's essence, greatly affecting its reception by the audience. However, translating film titles can be challenging since translators need to keep the original meaning of the source text and consider the acceptance of the target potential audience. Given its importance, there is a need to explore the features of film titles and the translation strategies employed. Most studies about film title translation have focused on the translation strategies from different perspectives without analyzing their features in adaptation. This paper aims to study the translation strategies employed in rendering film titles by utilizing Volkova and Zubenina’s (2015) pragmatic and socio-cultural adaptation theory, which is not used in film title translation. This study hopes to contribute to the burgeoning field of titleology and offers insights into adaptation's pragmatic and sociocultural dimensions. Hence, the findings are anticipated to enhance the understanding of adaptation processes and provide practical guidance for translators in the field.Keywords: film title, translation strategies, socio-cultural adaptation, pragmatic adaptation
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".