Transcreation and AI in global marketing
Bibliographic record
Abstract
Abstract This study investigates transcreation in global marketing through a comparative case study of selected sections of Apple’s and Sunstech’s multilingual websites (English<>Spanish). Using the FACT framework, it evaluates content adaptation, cultural localization, and linguistic strategies. Apple employs a hybrid strategy, blending standardization and transcreation, maintaining structural consistency while adapting key visual and textual elements for cultural relevance. Sunstech, however, relies heavily on standardization, with translation deficiencies affecting its cultural and linguistic resonance. The study also explores AI’s role in transcreation, highlighting its efficiency in handling large volumes of content. However, AI-generated translations often lack cultural nuance and creative adaptation, requiring human intervention to ensure cultural and contextual appropriateness. Ultimately, the study advocates for a hybrid transcreation approach that combines technology with human creativity, enhancing global brand communication and positioning in increasingly diverse and competitive markets.
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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 | 0.000 | 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 | 0.000 |
| 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 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".