Visual puns in the Arabic subtitled and dubbed versions of Shark Tale
Bibliographic record
Abstract
This piece of research, which is part of a project concerned with the translatability of figurative language in AV content from English into Arabic and vice versa, investigates the translatability of visual puns in the animated movie Shark Tale from English into Arabic in both its subtitled and dubbed versions. The data of this study consist of the original English film scenes and their Arabic subtitles and dubs. Based on Aleksandrova’s (2019) taxonomy, which treats pun translation as a cognitive game in the translator’s mind, it was confirmed that puns can be translated by accepting the game of translation using two different strategies: (a) Quasi-translation: where the translator preserves one of the signs of the original pun and replaces the other with a suitable one from the target language. (b) Free Translation: where the translator replaces the two signs of the source pun with new signs from the target language. It was also confirmed that the game of translation can be rejected by using Literal Translation where the translator literally translates the pun into the target language. Another minor issue raised in this study is that visual puns and complex puns that are culturally very local are subject to be ignored by No Translation, which is the omission of the linguistic host of pun. The current study concludes by providing some implications and solutions for translators dealing with pun in animated films.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".