Bibliographic record
Abstract
From the 1980s to the present, Hollywood’s major distributors have been able to redistribute U.S. theatrical attendance to the advantage of their biggest blockbusters and franchises. At the global scale and during the same period, Hollywood has been leveraging U.S. foreign power to break ground in countries that have historically protected and supported their domestic film culture. For example, Hollywood’s major distributors have increased their power in such countries as Mexico, Canada, Australia and South Korea. This paper will analyze a pertinent ‘test case’ for Hollywood’s global power: China and its film market. Not only does China have a film-quota policy that restricts the number of theatrical releases that have a foreign distributor (∼20 to 34 films per year), the Communist Party has helped the Chinese film business grow to have steady film releases and its own movie star system. Theoretically, China would be a prime example of a film market that would need to be opened with the assistance of the U.S. government. Empirically, however, the case of Chinese cinema might be a curious exception; we can investigate how a political economic strategy rooted in explicit power is reaching a limit. Hollywood is, potentially without any other option, taking a more friendly, collaborative approach with China’s censorship rules and its quota and film-production laws.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.001 |
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".