Bibliographic record
Abstract
This article investigates the ideological component of patronage in the subtitling of four South African soap operas: Generations , 7de Laan , Muvhango , and Isidingo . Taking the concepts introduced by Lefevere as point of departure, the article first discusses the various ways in which audiovisual translation (AVT) is subject to manipulation. This manipulation is shown to be a result of the fact that subtitles, as text superimposed onto the image during post-editing, thereby obscuring a small part of the screen, constantly foregrounds itself to the audience. This foregrounding is also affected by the linguistic background of the audience – whether or not they understand the original dialogue. The argument then turns to a discussion of AVT, and specifically subtitling, as rewriting. The link between language and ideology is discussed as it pertains to issues of power, particularly related to the role of English in the media, also in South Africa, where, in Gottlieb’s terminology, South Africa can be described as a multilingual anglophile context. The language policy of the South African Broadcasting Corporation is then discussed in terms of patronage and ideology followed by a discussion of the role of ideology in these four locally-produced soap operas. In this discussion the different ways in which the subtitling practices of the soap operas reflect ideology are investigated. The article concludes that accessibility plays a smaller role in subtitling in South Africa than the ideology of multilingualism and multiculturalism.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".