Visual Persuasion: Issues in the Translation of the Visual in Advertising
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it