On Translation of Advertisements from the Perspective of Culture/SUR LA TRADUCTION DE PUBLICITÉ DANS UNE PERSPECTIVE CULTURELLE
Bibliographic record
Abstract
Each language contains elements which are derived from its culture, such as idioms, proverbs, and other fixed expressions. Translation is not only an inter-lingual transfer, but also a cross-cultural communication. Along with the development of the economy and science, globalization is a more increasingly obvious phenomenon. There is is closer and closer relationship between countries. So the intercultural advertisements are becoming common. The inter-cultural advertisements transmission is to communicate with the consumers from different regions, different nations, different countries and different societies. And the politics, economy and cultural situations in these areas are different from those in their own nations. And in all of these differences, the difference between cultures is the one that has the greatest and the most direct influence on the transmission of advertisements. If one wants to do well in advertisements translation, one must have a good knowledge about the culture whose language is his target language. And in the transmission of advertisements, knowing what one should do is as important as knowing what one should not do. In this paper, the author takes into consideration the cultural factors that affect the translation of the intercultural advertisements and then offer some suggested strategies when doing the translation.
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.
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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.023 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".