English in Korean Advertising: An Exploratory Study
Bibliographic record
Abstract
The use of English language names, titles, and catchphrases are often presented in advertisements that do notnecessarily target English-reading clientele in South Korea. This paper explores the functionality andcharacteristics of English text found in Korean promotion. This was a multistep exploratory study of the use andacceptance of English in Korean advertisements. First, various Korean media sources were scrutinized todetermine the percentage of promotions that exhibited English and how it was utilized. Second, a surveyregarding the acceptance and perception of English in these promotions by the Korean consumer was conducted.Third, a vocabulary test of the most common English descriptive words utilized in Korean magazineadvertisements was given to Korean business students. It was determined that 59.5% of the advertisementscontained English words. The survey revealed evidence that English in Korean promotions is well received withthe majority agreeing that the English language is novel or exoticness. The twenty most commonly foundEnglish words presented in Korean magazine advertisements were only understood 58.5 % of the time by thebusiness college students surveyed. This study shows that international and native Korean firms are havingsuccess in the Korean market by using marketing that integrates English has a means to show style and appeal tothe Korean customers. The findings suggest that the Korean consumer finds the use of English to be appealingregardless of their comprehension of the language itself.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".