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Comparing translation strategies in Netflix subtitles and generative AI: Nickname translation in Culinary Class Wars

  • The Journal of Translation Studies
  • Abbr : JTS
  • 2026, 27(3), pp.217~255
  • DOI : 10.15749/jts.2026.27.3.008
  • Publisher : The Korean Association for Translation Studies
  • Research Area : Humanities > Interpretation and Translation Studies
  • Received : August 15, 2026
  • Accepted : September 15, 2026
  • Published : September 30, 2026

Park, Kunyoung ORD ID 1

1경희사이버대학교

Accredited

ABSTRACT

This study compares how Netflix’s official subtitles and ChatGPT (version 5.4 mini) render the nicknames of "black spoon" chefs in the Korean Netflix series Culinary Class Wars (Seasons 1–2). The dataset comprises 100 Korean-English nickname pairs (57 from Season 1 and 43 from Season 2) extracted from the first three episodes of each season. The nicknames were classified using a framework adapted from Aixelá's (1996) taxonomy of strategies for culture-specific items, distinguishing conservation strategies (non-translation, transliteration, and literal translation), substitution strategies (reduction, addition, partial modification, and total modification) and mixed strategies. The data were also grouped according to whether the nicknames posed particular translation challenges, including cultural references, humor, wordplay, and intertextuality, and were analyzed both quantitatively and qualitatively. The findings show that Netflix’s subtitles generally preserved straightforward nicknames while favoring substitution strategies, especially partial and total modification and reduction, in more challenging cases. ChatGPT, by contrast, showed a strong preference for literal translation across both straightforward and challenging items and made little use of reduction or omission. This tendency remained evident in Season 2 even after feedback was provided on ChatGPT’s Season 1 translations, pointing to a persistent difference between human and AI approaches to nickname translation. Despite its focus on a single case, the study offers practical insights into the strengths and limitations of generative AI for audiovisual translation.

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