Translating adult-oriented humour in children’s animated movies from English into Turkish: A corpus-based study*
Bibliographic record
Abstract
The aim of this study is to examine practices in translating adult-oriented linguistic humour in children’s animated movies. The research presents a corpus-based mixed study which draws insights from humour translation, translation of children’s literature (specifically the address problem) and audiovisual translation. The corpus-data were collected from forty randomly chosen Hollywood-made animated movies released between 2010-2019. In order to distinguish a humorous instance as adult-oriented, Akers’ (2013) adult humour categories were applied. The translation strategies applied to the target movies were categorised in accordance with their functions as retainment, replacement and omission. The classification of translation strategies used in this study was developed relying on the available translation strategies of Delabastita (1996) for puns, Leppihalme (1997) for allusions and Mateo (1995) for irony. Further, the data were interpreted both qualitatively, according to Asimakoulas’ (2004) theoretical model for the translation of humour, and quantitatively. The analysis revealed that to preserve adult-oriented humour, the most successful translation strategies belong to the Replacement Set while the least successful set is the Omission Set. According to the overall results, the general tendency is towards the elimination of adult-oriented humour.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".