On the Link Between the Success of K-pop Groups in the European and American Markets and the Consumer Preferences of the Fan Base
Bibliographic record
Abstract
This study analyzes the K-pop craze that is spreading globally, as well as analyzes the success of K-pop that has broadened to Europe and the United States, not only in Asia. In order to do so, this paper analyzes the effective strategies needed to further develop K-pop in Europe and the United States after observing the success of the existing K-pop in Asia and China, based on actual cases, and analyze the important reasons for the success of K-pop, which are the entertainment company (systematic production by planning companies and systematic communication methods), the active use of social media, the consumers (the love and preference of the huge fan base), and the performers (perfection of singing, choreography and image). On the other hand, nowadays, as K-pop continues to broaden its market in Europe and the United States, it is possible to find the tipping point of a tendency towards homogenized music and insufficient characteristics of K-pop's strong commerciality and insufficient systematic business model, in order to overcome this phenomenon and thus to gain insights from existing success stories.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".