Performing (Homo)Sexual Citizenship Under Authoritarian Rule: Gay Couple Vlogs and Everyday Intimacy in China
Bibliographic record
Abstract
This article explores fufu (夫夫) vlogs—user-generated video diaries of Chinese gay male couples on Bilibili—as performative acts of (homo)sexual citizenship under authoritarian rule. Drawing on frameworks of sexual, cultural, and performative citizenship, the study examines how these vlogs negotiate relational recognition, legal marginality, and mediated visibility within a tightly censored digital ecology. Combining digital ethnography and reflexive thematic analysis, the article demonstrates that fufu vlogs simultaneously reproduce and resist heteronormative ideals, offering emotionally legible yet normatively constrained depictions of gay life. While these performances often align with conservative scripts of monogamy, domesticity, and filial piety, they also tactically inhabit legal loopholes—such as the Assigned Guardianship System and hukou affiliation—to enact forms of symbolic and relational legitimacy. Crucially, viewer interactions through danmu and comment threads constitute informal pedagogical spaces, circulating information and cultivating civic awareness. The article conceptualizes these mediated practices as performing Chinese authoritarian (homo)sexual citizenship—a mode of gay belonging negotiated through ambivalent performances of public visibility, normative proximity, and strategic intimacy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".