Translating Mad Cow Disease: A Case Study of Subtitling for a Television News Magazine
Bibliographic record
Abstract
This paper explores how discourse is reframed in audiovisual translation in a well-known South Korean television news magazine, PD Swuchep [ PD Notebook ]. The episode under consideration raised serious questions regarding the safety of US beef and the conduct of South Korean officials responsible for negotiating imported beef in the Korea-US Free Trade Agreement talks. The program, which contained sound bites of interviews in English subtitled in Korean, created uproar in the South Korean society and played a significant role in touching off many months of massive street rallies against the government for its alleged sloppy handling of the beef import negotiation talks. Based on the view that subtitling for television news is a practice of “entextualization,” the study argues that (1) different degrees of discursive transformations in the target text cumulatively work to support and exaggerate the risk of the transmission of mad cow disease as a result of eating American beef; and (2) the discursive transformation is reinforced by institutionally defined roles and procedures for target text production. The findings suggest that one of the main criteria for the selection of target text expressions may be the narrative relevance of the political slant of the translation to the story of the program. Furthermore, the narrative of the target text may not necessarily be consensually co-constructed by participants. On the contrary, it is often a product of conflict-ridden processes that are characterized by tensions and differences in power relationships among people in different roles in the media institution.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.025 | 0.013 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".