Clearing the Smoke to See the Screen: Ideological Manipulation in Audiovisual Translation
Bibliographic record
Abstract
In this article, a critical and methodological approach is offered concerning the subject of manipulation and translation in the realm of the audiovisual. Taking the potentialities unleashed by thecultural turnin Translation Studies as a starting point, the paper first provides an overview of the main hurdles and issues at stake when adopting a line of enquiry centred around the realisation that the way in which cultural values are translated depends not only on linguistic asymmetries between languages but also on fundamental decisions based on power, dominance, and ideology. As part of a debate that could prove fruitful in the world of audiovisual translation (AVT), the concept of manipulation is discussed in detail and a distinction between technical and ideological manipulation is put forward. After considering the special case of censorship and some of the new developments in the use of subtitling as a tool for local empowerment, it is suggested that the boundaries of research into AVT should be pushed beyond its traditionally parochial linguistic sphere by focussing more on unmasking the rationale behind ideologically motivated changes and by contextualising them within a wider socio-cultural environment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.014 | 0.085 |
| Scholarly communication | 0.023 | 0.019 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".