Subtitling Strategies of Verbal-Visual Components in The Social Dilemma
Bibliographic record
Abstract
The widespread of technology and streaming platforms have highlighted the importance of audiovisual translation. Audiovisual materials consist of four components: verbal-acoustic, verbal-visual, nonverbal-visual, and nonverbal-acoustic. Subtitlers usually focus on rendering the verbal-acoustic component (i.e., the dialogue) and rarely focus on the verbal-visual component. Therefore, this article aims to identify the subtitling strategies used in rendering the verbal-visual components that are displayed simultaneously with the dialogue. The data are taken from the docudrama The Social Dilemma. Gottlieb’s (1992) typology is used to identify the applied subtitling strategies. The analysis demonstrates that the subtitler employed seven subtitling strategies: transfer, paraphrase, imitation, deletion, expansion, condensation, and resignation. Also, the results indicate that resignation was the most frequently used strategy. Finally, the study recommends that the subtitler considers all components in the subtitling process.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".