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.
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 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".