<em>Tielian</em> in Mixed Realism: Real Person Fan Fiction and Collective Memory in Contemporary Chinese Fandom
Bibliographic record
Abstract
This article investigates the question of authenticity in Chinese real-person fan fiction (RPF), a fan-fiction subgenre centered on real-world celebrities. Through the lens of tielian (“getting close to the face”) and mixed realism, the study examines how Chinese RPF authors craft recognizable representations of celebrity characters that resonate with fan readers. Focusing on the RPF work Light Rail Does Not Reach the Eighteenth Floor on the fostering idol, the case illustrates how real-world events are transformed into compelling depictions of the past that weave fragments of realities into a coherent narrative. In this context, tielian serves both as a measure of authenticity and as a means of expanding readers’ consumption of celebrities’ media-generated realities. Since collective memory plays a crucial role in the fostering-idol fandom, subjective interpretations often contest each other in the agreement of tielian. This tension highlights the dynamic negotiation of authenticity within RPF communities. By framing tielian as a form of processual and affective truth making, the article positions RPF as a site of literary innovation that reconfigures realism, authorship, and authenticity in participatory digital culture.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".