The Real Impact of Subsidies on the Film Industry (1970s-Present): Lessons from France and Korea
Bibliographic record
Abstract
Many countries are becoming interested in developing their film industries as a way of promoting their national culture and increasing their soft power. With the continued global dominance of Hollywood films, policy makers are increasingly considering government subsidies as an essential tool in promoting their national film industries. However, the actual effectiveness of subsidies in promoting a film industry remains debatable. In order to better address this issue, this paper evaluates and compares the experiences of France and Korea. Both countries have adopted exactly the same sequence of instruments—import quotas, screen quotas, and then subsidies—yet have applied almost the opposite subsidy policies. Since the 1950s, France has intensively used subsidies while Korea has not. After more than a half century, these different subsidy policies have led to very different outcomes. This paper shows that a film industry without significant government subsidies can prosper better in the long term than a heavily subsidized one. This is an important lesson for countries that want to develop their film industry and to promote their culture by designing effective film policies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".