Bibliographic record
Abstract
Hallyu fans are people who are dedicated to popular culture in South Korea, including music, drama and film. This study focuses on fans of Korean pop music, which is known as K-pop. Developments in digital communication technology have given rise to media such as forums, websites, video channels, and fan sites that are consumed by K-pop fans. Fans participate in multiple fandoms because these websites are easily accessed by public audiences. However, problems arise when fans start to compete, using their knowledge to help validate their existence and to help the perception of authentic identities within fan communities. This paper is based on virtual ethnographic fieldwork that identified fans’ constructions of their own identities and the building of a social hierarchy through various online practices. The research findings are based on four months of fieldwork with two online Hallyu fandoms; ELF (Ever Lasting Friends) and A.R.M.Y (Adorable Representative M.C. for Youth). The findings reveal that conflicts exist in certain fandoms which aid in defining fan identities and, at the same time, fans undertake positive socialising actions which contribute to the fandom itself. Interactions between fandoms also need to be recognised, since online fandoms can be seen as borderless.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".