Article: Auditioning for Hollywood: A Comparative Study of Tax Incentives Offered to the Film Industry
Bibliographic record
Abstract
The European film landscape is characterized by a strong presence of Hollywood productions. In 2019, American productions held approximately 70% of the market within the European Union while European productions had 25%. As a response, the EU has introduced differing types of financial support schemes with the aim of offsetting the imbalance between the American and the European film industries. This article describes and analyses tax incentives offered to the film industry from two main lines of inquiry: (1) a comparative and empirical tax study of twelve jurisdictions (Canada, China, France, Germany, India, Italy, Japan, the Netherlands, Norway, Spain, the United Kingdom, and the United States) in which the design of such tax incentives is investigated, and (2) a conceptual tax policy discussion on how states may design and implement such tax incentives. Tax incentives, sustainability, development, film industry, tax competition, tax design, comparative tax law
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".