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Record W4408810965 · doi:10.1177/13548565251328765

‘You’re not invited’: Negotiating feminism within digital public sphere surrounding Lisa’s exotic dance

2025· article· en· W4408810965 on OpenAlexaff
Trang-Nhung Pham, Phuong Anh Tran-Mai

Bibliographic record

VenueConvergence The International Journal of Research into New Media Technologies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicGender, Feminism, and Media
Canadian institutionsUniversity of OttawaGlobal Affairs Canada
Fundersnot available
KeywordsDanceFeminismNegotiationPublic sphereMedia studiesVisual artsGender studiesArtSociologyPolitical scienceSocial scienceLawPolitics

Abstract

fetched live from OpenAlex

This paper presents an examination of the online backlash against Lisa, a member of the K-pop girl group Blackpink, triggered by her decision to perform in a burlesque club. It explores how feminist counterpublics operate within Asian fan communities in discussions concerning the visibility of female celebrities. Employing critical discourse analysis (CDA) in our study, we examined gender, power, and ideology in feminist discourse surrounding Lisa’s action, paying close attention to how feminism is negotiated within counterpublics of online communities. Analysis of 22 Facebook posts and accompanying comments from Vietnamese K-pop fan groups and pages revealed how feminist discourse in Vietnamese online fan communities is deeply intertwined with cultural preservation, social responsibility, and class considerations. Reflecting broader societal tensions between individualism and collectivism, as well as between Western and Asian values through varied interpretation of feminism within a discursive framework, this contextualization builds on existing scholarship of how feminist discourse is shaped and contested in non-Western settings.

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)0.005
metaresearch head score (Gemma)0.006
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 score0.014
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.020
Scholarly communication0.0080.005
Open science0.0010.005
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.188
GPT teacher head0.415
Teacher spread0.227 · 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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