Censorship and Creative Communities: Fragility and Change of Fanfiction Writing in China
Bibliographic record
Abstract
Abstract Research on cultural production has recognized that artistic creation, especially fandom subcultures, depends on social interaction within artworlds. Yet less research has examined how creative production functions when exogenous social forces disrupt key forms of interaction. This study leverages the case of Chinese fanfiction writers’ response when state censorship interrupts and threatens fanfiction writing to better understand the vulnerability of creative communities. Based on interviews with Chinese fanfiction writers who experienced an unexpected intensification of online censorship in 2020, and following fandom studies in understanding fanfiction as rooted in a gift economy, I show how censorship discouraged writing by destabilizing interaction and interfering with gift exchanges. I find that censorship transformed cultural production by (1) reorganizing and fragmenting networks, (2) reshaping the meaning of visibility, and (3) opening up new opportunities in a disintegrated community. As this study argues, we need to go beyond asking whether censorship is effectively destructive or not. While creative communities are vulnerable to outside disruption, especially in online space, the pressure of censorship leads to new conventions, networks, and fields for artistic creation as censorship does not simply strangle creativity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".