Bibliographic record
Abstract
This paper explores the wider issue of translation training in multimodal contexts. The multimodal text represents a complex semiotic canvas on which the various systems of signification (verbal, images, colour, layout, etc.) interact in complex ways to produce a coherent meaning. Such interactions affect translation students’ understanding of multimodal texts and as such their training must also be visually-oriented in order to improve their translation efficiency when dealing with these texts. The paper is primarily (though not exclusively) concerned with the print multimodal text, and examines how the various aspects of the visual semiotic elements affect the teaching of its translation into another language. One such aspect is the new challenges that have been imposed by the visual on the field of translation studies. A second aspect is the visual implications for translation trainers and students. A third aspect is the wider multimodal context in which they have been found and involves the necessary multimodal approach to translation training, the development of a relevant awareness of multimodal texts and a number of other issues such as students’ creativity and the role of the subject specialist in the translation classroom. Finally, suggestions are made for further development of relevant teaching areas that are driven by the visual aspect of the multimodal text.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".