Copy Adaptation, or How to Translate a Source Product for a Target Market
Bibliographic record
Abstract
The aim of this paper is to show the significance of product type and potential buyers’ expectations in ad translation for the production of commercially effective target versions. To this end, this study focuses on adverts for computer products as the referent of the advertising message, and computer users as the addressees. The cultural contexts in contact are the USA and the Spanish markets for these products. The source texts (ST) are adverts from popular USA computing magazines, while the target texts (TT) are from the same type of publications published in Spain. In order to measure the relevance of product type in the translation of these ads, a comparison of the STs and the TTs has been carried out in terms of verbal and non-verbal elements. The strategies used in the translation of these two groups of elements have been described and identified. The results obtained through the analysis of this corpus will shed light on (1) whether the shifts produced in the TTs respond to Spanish computer users’ expectations, that is, whether they are in line with the features of computing ads in Spanish, and (2) whether there are shifts clearly related to the product type.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.041 |
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".