The Role of Images in the Translation of Technical and Scientific Texts1
Bibliographic record
Abstract
The information transmitted by images accompanying technical and scientific texts id supposed to lead to a better, visually-oriented understanding of the concepts and descriptions contained in the text. Given that conceptualising scientific information entails creating mental representations of the concepts, and since the function of images is complementary to the function of texts, cultural attitudes might well influence many aspects of an image ranging from its contents, to shape or colour. One might ask if the translation process should include the adaptation of original images in the text in order to avoid a misunderstanding of the message by readers from a target culture, or if images should be treated differently depending on the target audience. As images in technical and scientific books and articles are often difficult to interpret a close study of them is needed so that the visual message matches the knowledge of the receiver in the target culture. In this article we propose cognitive and pragmatic criteria regarding the message transmitted by images in scientific texts as a guide to translation-oriented image analysis.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.026 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".