Bibliographic record
Abstract
This paper discusses the impact of fan culture on the Chinese film industry. The continuous advancement of technology has changed the nature of ‘fan’ culture, which is now a daily presence in public life and, both directly and indirectly, exerts influence on the mass entertainment industry. A new, distinctive industry that has emerged from this new culture is fan films, or commercial films oriented toward a specific ‘fandom’ and that seeks to take advantage of a franchise’s cultural capital, reaping the benefits of fan attention. The ‘fan film’ model has even begun to influence the Chinese film industry, with films featuring certain idols gaining automatic support from a large number of fans, regardless of film quality. However, public opinion of these films is often polarised, For the main audiences of commercial and fan films, they will think that these movies that meet their tastes deserve a high rating. But there is also a part of the movie-viewing group that thinks the production and content of these movies are not sophisticated and deep enough to deserve a good rating. To understand this complex phenomenon, a deeper look into fan culture is necessary, specifically its characteristics, impact on the film industry, and how the film industry in turn manipulates fan culture to its benefit. This study will therefore seek to see how movies, stars and fans work together to promote China’s film industry using both sociological and communicative analysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".