‘Between losing my family and asserting my identity’: exploration of Korean LGBQ YouTubers’ experiences coming out to parents
Bibliographic record
Abstract
This study aimed to explore Korean Lesbian, Gay, Bisexual, and Queer (LGBQ) individuals’ experiences of coming out (i.e. disclosing one’s LGBQ identity) to their parents through analysing YouTube videos by Korean queer creators sharing their stories about coming out to their parents. Among the initially identified 23 videos, nine were chosen based on specific criteria. Reflexive thematic analysis revealed four themes: (a) I had to negotiate between the fear of losing my family and asserting my identity, (b) I armed myself with knowledge to confront parents’ prejudices, (c) my parents were initially fearful, but ultimately wanted me to be happy, and (d) it is important to be thoughtful about coming out even though it brought me closer to my family. This study underscored the complex interplay of Confucianism and Christianity as social contexts and the creators’ dual strategy of employing cultural tactics and critically confronting biases in response. For Korean LGBQ YouTubers, coming out was a highly relational endeavour. This study highlighted social media’s role in promoting queer visibility in South Korea. Implications include the need to develop more culturally appropriate support and resources tailored to Korean LGBQ individuals and to facilitate greater understanding and acceptance of LGBQ people among Korean parents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".