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Record W4413187032 · doi:10.1177/13634607251362047

“Frogs don’t remember when they were tadpoles”: searching for love in South Korea’s “cyber gay ghetto”

2025· article· en· W4413187032 on OpenAlexaff
J. Cho

Bibliographic record

VenueSexualities · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicAsian Culture and Media Studies
Canadian institutionsUniversity of British Columbia
FundersAcademy of Korean Studies
KeywordsPsychoanalysisPsychologyGender studiesArtSociology

Abstract

fetched live from OpenAlex

This paper examines the contradictory role of the Internet in shaping the cisgender gay male community in the non-liberal and communitarian context of South Korea. Despite its global status as a cultural powerhouse with one of the world’s most networked economies, Korea is notorious for its strict regulation of gender roles and sexual conduct. Since the mid-1990s, its gay population has utilized the BBS (Bulletin Board Service) and the Internet to evade familial surveillance and forge discreet gay lifestyles. Nonetheless, challenging the liberatory discourse of the Internet associated with the worldwide phenomenon of “queer globalization,” this technology is now contributing to the mutual estrangement of gay men from each other and the broader Korean society. Not only do they have to jump through the hoops of each other’s reified sexual preferences to connect with the community organized as a neoliberal market, they have to contend with the pressures of conforming to a hetero-masculinist culture based in the overlapping structures of family, work, and military that remain hegemonic in South Korea. Unable to suture this gap between a Korean family-based reality and a white Euro-American gay lifestyle promoted on the Internet, many gay men become stranded as “small tribes within a cyber ghetto.”

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0180.011
Scholarly communication0.0080.009
Open science0.0010.005
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.049
GPT teacher head0.359
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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