Visual Aspects of Intercultural Technical Communication: A Cognitive Scientific and Semiotic Point of View
Bibliographic record
Abstract
This article presents a cognitive scientific view of the role of verbal and visual aspects in the translation of technical texts. The semiotic differentiation between symbols and icons is discussed from both the cognitive scientific and the translation studies perspectives. The article refers to the definition of text as a unit of communication (and translation) that includes both verbal and visual aspects and thus emphasises the function of the visual dimension in translation. The theoretical discussion is related to professional practices in the modern translation workplace: through its discussion of the results of a recent empirical field study based on participant observation over an extended period of time in a translation agency, the present research seeks to determine the extent to which the visual dimension can be taken into account in technical translation. In particular, the results point to the consequences of the use of translation technologies (translation memories, translation management systems, localisation software, etc.) in the modern translation workplace. The dominance of the verbal aspect induced by the use of some such technologies can make it increasingly difficult for translators to pay attention to the visual elements in translation. This stresses the importance of the inclusion of courses on the critical and professional use of translation technologies within translation studies programmes.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.037 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".