Visual Persuasion: Issues in the Translation of the Visual in Advertising
Bibliographic record
Abstract
This contribution is concerned with the decoding of advertising messages and the question of whether and how such messages are received by members of other cultures. The answers to these questions are important when considering the role of the translator in adapting global campaigns. Most advertisers concentrate on avoiding linguistic pitfalls when adapting advertisements for new markets, but in any advertisement, consumers are primarily attracted by visual elements. It can be said that an advertisement’s potential for triggering a train of connotations in the consumers’ minds is the most important aspect of advertisement design. According to Barthes, images are polysemous, but it is not clear whether all connotations are accessible to viewers in different cultures. The visual in advertising exploits the original and the stereotypical – novelty attracts attention, while the stereotypical serves as a reference to established knowledge. The main design options discussed are layout and directionality, as well as the choice of subject, which also allows a range of visual rhetorical options to be encoded. Decoding depends on practical, cultural and aesthetic knowledge. The challenge to the translator lies in assessing whether the choices made in the original advertisement and its connotation potential can be transferred to a new language market with different cultural practices. The analysis draws on the semiotics of Barthes, and presents more recent approaches from cultural studies. It is illustrated by examples of the strategies adopted for global advertising campaigns by companies operating world-wide and includes a case study on advertising in China.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.044 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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".