Problems in English to Arabic Subtitles Translation of Religious Terms—Bruce Almighty and Supernatural on MBC & Dubai One: A Case Study
Bibliographic record
Abstract
This paper attempts to shed light on some cultural and/or technical problems in the translation of religious terms from English into Arabic in the subtitles of movies, with particular reference to some Arab Gulf countries channels. Due to limitations of time and space, the researcher has taken two particular channels as representative, namely MBC Channel group and Dubai One. The data of the research have been collected from one film and one TV series as quite typical examples of works that may lead to serious problems in the subtitles translation with regard to religious terms. In both cases, the use of religious terms is not only obligatory but also focal. The researcher points the discrepancies in the choice of Arabic equivalents for the English religious terms in question as well as explores the possible reasons of and recommended solutions to such cultural problems in translation. The film, Bruce Almighty, is a light and comic treatment of the phenomenon of well-educated yet vain young men, doubting the wisdom of God Almighty. Jim Cary plays the role of the young man, while Morgan Freeman actually plays God! Hence, there is no easy way out of the necessity of tackling the problem of translating the religious terms involved. As for the TV series, Supernatural, the whole episode deals with God, angels, demons and Satan. It remains to be said that this paper does not claim to give decisive answers to the questions posed by the research, but only aspires to pave the way before further research on the topic and related issues.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".