‘You betrayed us’: Ethnic celebrity gossip in diasporic women’s online communities
Bibliographic record
Abstract
This article examines a relationship between ethnic celebrities and diasporic communities by focusing on one case of Korean diasporic women gossiping about Korean actress Seo Min-jung. After a 10-year hiatus following her sudden migration to the United States and marriage to a Korean American dentist in 2007, Seo made a successful comeback to show business by starring in Korean reality shows and opening her Instagram account. Seo’s struggles as a Korean immigrant woman/housewife/mother, portrayed in TV shows and on Instagram, positively resonated with diasporic Korean women’s online communities (DKWOC). This positive discourse around Seo, however, transformed into celebrity bashing when her Instagram scandal happened in 2019. We trace the change of gossip around Seo in DKWOC concerning Korean diasporic women’s identity and status. We argue that DKWOC members’ gossiping of Seo functions as a way of coping with their situation, as they come to recognise the class difference between themselves and Seo and feel disempowered by their dissatisfying circumstances as immigrants.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".