Fandom and coercive empowerment: the commissioned production of Chinese online literature
Bibliographic record
Abstract
This article examines how the relationship between consumers and producers of cultural products is shaped by the proprietary nature of digital platforms. Drawing on 4 years of online observation and analysis, we examine the relationship between the producers of online Chinese fiction, amateur writers, and their consumers, that is, the fan communities of readers who respond to their work. Enabled by Chinese literary websites, readers act like sponsors who provide emotional and financial incentives for writers to produce online fictions by commenting, voting, and sending money. Readers become actively involved not just because of the content of the stories but because they form strong commitments to stories and their writers, and gain reciprocity and a sense of self-determination during the interactional process. We argue that although writers are freer from state control online, they are still beholden to the whims of their fans because of what we call the commissioned production of fictions. We contribute to fan community studies by analyzing how commercialized website settings structure the strategies available to participants, how these settings affect the content of the cultural products, and how the Chinese historical and cultural contexts impact the dynamics of the online community.
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 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".