The Deliberate Non-Subtitling of L3s in Breaking Bad: A Reception Study
Bibliographic record
Abstract
This paper presents the results of the first empirical reception study on the deliberate non-subtitling of L3s in the multilingual TV series Breaking Bad . Multilingual films and TV series are on the increase both in terms of success and penetrating wider audiences in a global market. This puts the focus on how multilingualism is conveyed to the audience and how audiences respond to it. While the translation strategies used in multilingual productions have received some attention, audiences’ reactions to them have only been investigated through an analysis of comments posted on an online movie message board. This study presents the results of a survey on the perception of and response to non-translation of L3 segments in a multilingual prestige TV series among hearing viewers. It shows that audiences are not only acutely aware of deliberate non-translation but also actively seek to identify motivations for it, which are context-sensitive and largely coincide with the filmmakers’ motivations for this practice. On the translation-theoretical side, this paper suggests that Corrius and Zabalbeascoa’s (2011) framework for the translation of L3s in dubbing would benefit from a supplement for other translation modes. On the applied side, the findings of this empirical reception study can inform agents in the international film and TV industry about audiences’ viewing preferences and potentially change AVT practices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".