Bibliographic record
Abstract
long-term restructuring of China's film industry shows that Hollywood is greeted not with simple hostility, but rather with ambivalence, as befits a voracious rival that has much to offer. Repelled as many are by Hollywood's persistent and aggressive entry into the Chinese film market, there are also good reasons why world leaders in both politics and film want to make deals with the pushy Americans. This study uses a political economy approach to examine the relationship between Hollywood and Chinese cinema.' Most analyses of Chinese film follow the tradition of cultural criticism, where films are read as texts for insight about Chinese culture.2 This artist-centred approach is an important source of understanding of change in Chinese society, and of the position of cultural creators within it. Although we believe this perspective is appropriately central to studies of Chinese film, it does not address certain questions well. These include the Hollywood challenge, which comes amidst important structural changes in the production, distribution and exhibition of Chinese film.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".