A Translational and Narratological Approach to Audio Describing Narrative Characters
Bibliographic record
Abstract
The present article examines two issues in the field of audio description (AD) that so far have received little attention. Research in AD is highly multi- and interdisciplinary, and while semiotics, discourse analysis, narratology and film studies are some of the frameworks that are regularly used to study audio description, few attempts have been made to frame AD within the field of translation studies (TS). The first part of this article, therefore, describes a functionalist approach to audio description, both to strengthen the position of AD within TS and to contribute to a systematization of AD research. In the second part, such a systematic approach is applied to one question deemed “vital” in many national AD guidelines: the audio description of characters. Based on principles from formal narratology, a strategy is developed that allows describers to analyze characters in a structured, general way and to select relevant character information to be included in their descriptions.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".