Audio Description with Audio Subtitling for Dutch Multilingual Films: Manipulating Textual Cohesion on Different Levels
Bibliographic record
Abstract
There is a strong trend towards multilingualism in Flemish and Dutch films today. In order to make such films accessible for a blind and visually impaired audience, the audio description (AD), which supplies the information from the visuals that cannot be accessed by this target audience, must be combined with audio subtitling (AST), for the translation of the dialogue. Today, a wide variety of strategies is used to accomplish this form of textual manipulation, but current practice is largely based on intuition. The present paper reports on the first phase of a research project carried out on four films, in collaboration with the AD scriptwriter and the sound engineer responsible for the recordings of the Dutch films with AD and AST, two of which will be considered here: Oorlogswinter (Winter in Wartime 2008) and Tirza (2010). The project makes use of four films, but due to limits of space we focus on two only, aiming to reply to three questions. First, we look at how the AST is inserted and whether it interacts with the films’ foreign language dialogue exchanges. Then we consider whether intonation contributes to the coherence of the text. To conclude, the audio and written subtitles are compared. Finally, suggestions for further research are provided.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".