The Translatability of Interjections: A Case Study of Arabic-English Subtitling
Bibliographic record
Abstract
This paper examines the translatability of Arabic interjections into English subtitling, illustrated with a subtitled Egyptian film, State Security subtitled by Arab Radio and Television (ART). Theoretical framework regarding both Audiovisual Translation (AVT) and interjections is first discussed. The significance of interjections is approached from the perspective of technical and translation paradigms. The study shows that although technical issues limit the subtitler’s choices, they have very little to do with translating interjections because they are typically short words. With regard to translation, the study shows that the subtitler may opt for three major translation strategies: 1) an avoidance of source language (SL) interjection whereby a SL interjectional utterance is translated into a target language (TL) interjection-free utterance; 2) a retention of SL interjection in which SL interjection is rendered into a TL interjection; and 3) an addition of interjection whereby SL interjection-free utterance is translated into a TL interjection.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".