Bibliographic record
Abstract
As an emerging form of intermodal translation, audio description (AD) raises many new questions for Translation Studies and related disciplines. This paper will investigate the question of how the coherence of a multimodal source text such as a film can be re-created in audio description. Coherence in film characteristically emerges from links within and across different modes of expression (e.g., links between visual images, image-sound links and image-dialogue links). Audio describing a film is therefore not simply a matter of substituting visual images with verbal descriptions. It involves ‘translating’ some of these links into other appropriate types of links. Against this backdrop, this paper aims to examine the means available for the re-creation of coherence in an audio described version of a film, and the problems arising. To this end, the paper will take a fresh look at coherence, outlining a model of coherence which embraces verbal and multimodal texts and which highlights the important role of both source text author (viz., audio describer as translator) and target text recipients in creating coherence. This model will then be applied to a case study focussing on the re-creation of various types of intramodal and intermodal relations in AD.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".