Towards a Multimodal Pragmatic Analysis of Film Discourse in Audiovisual Translation
Bibliographic record
Abstract
This paper is about the introduction and use of Multimodal Pragmatic Analysis (MPA) as a research methodology in audiovisual translation (AVT). Its aim is to show the contribution of the MPA to the analysis of film discourse in AVT with a focus on interlingual subtitling. For this purpose, the paper is divided into five sections which elaborate on the theoretical and practical aspects of the MPA methodology. Following the introduction, the second section defines the context of MPA as a new research methodology in AVT at the level of approach, design and procedure. The third section describes the theoretical base of this methodology, and the fourth examines its basic components and levels of analysis. The fifth section provides two practical examples to show how the MPA methodology operates in the analysis of speech acts appearing in the source text and the target text. Finally, the last section first discusses the advantages and disadvantages of the methodology and then concludes the paper with some suggestions for further research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".