Subtitling Taboo Language: Using the Cues of Register and Genre to Affect Audience Experience?
Bibliographic record
Abstract
Using French-English/English-French examples, this article puts forward the hypothesis that, in the film genre of social realism (depicting low socio-economic groups), subtitlers use linguistic and visual cues which are embedded in genre to trigger audience reactions to representations of taboo language. Examples of the subtitling of taboo language are drawn from three films and the hypothesis above will be explored along three main interrelated axes: i) the value of treating subtitles as an entire system; ii) the relationship between the specific film genre of social realism (depicting low socio-economic groups) and audience perceptions of taboo language use; and iii) discourse representations through register and its effect on characterisation. Nuances are brought to evidence from existing research which argues that the choices relating to taboo language made in the oral to written mode shift are subject to politeness restrictions in terms of register, and that these choices have a homogenising/levelling effect on characterisation (Lambert 1990; Taylor 2006a; Mailhac 2000 for example).
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".