Indirect (pivot) audiovisual translation: a burning issue for research and training
Bibliographic record
Abstract
This article serves as an introduction to the special issue on the practice of translating audiovisual content through an intermediate language or text. This increasingly common yet underexplored area presents numerous challenges and opportunities for research and training. By employing a broad definition of this practice, we aim to highlight its significance and complexity. We start by discussing the rationale behind focusing on this topic, noting the conceptual ambiguities and diverse terminology associated with it. Then, we review past, present, and anticipated developments in the field, and provide an overview of the contributions within this special issue. To conclude, we identify research questions and potential future directions, emphasising the need for continued exploration and reflection. Ultimately, with this introduction and the special issue as a whole, we aim to bring attention to this critical practice in audiovisual translation, encouraging further scholarly inquiry and practical advancements.
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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 | 0.035 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".