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Record W7127229078

Cashing In or Giving Back: The Role of Film Tax Incentives in the Cultural Impact of Runaway Productions

2025· article· W7127229078 on OpenAlexaboutno aff
Karolina Krok

Bibliographic record

VenueScholarship @ Claremont (The Claremont Colleges) · 2025
Typearticle
Language
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsTax incentiveIncentiveInternationalizationHollywoodDiversity (politics)Cultural diversityProduction (economics)Representation (politics)
DOInot available

Abstract

fetched live from OpenAlex

This thesis explores the internationalization of Hollywood through economic and creative runaway productions driven by international tax incentives. It examines how these incentives can impact not only the commercial but the cultural success of productions filmed abroad, focusing on regions such as Australia, New Zealand, Canada, the United Kingdom, France, Germany, California, New York, and New Jersey. The analysis centers on cultural tests and diversity requirements, revealing two main approaches: the first requires a minimum of diversity standards and cultural tests points to be met, while the second prioritizes and rewards productions with the highest achieved assessment of those. Furthermore, the paper critiques past approaches, including the perpetuation of stereotypical portrayals and extractive production practices, emphasizing the need for policies that recognize meaningful representation and community engagement. Ultimately, this research concludes that incentivizing cultural contributions is the future of tax incentives, positioning them as a powerful policy tool for shaping global, culturally impactful, and authentic storytelling, while generating substantial economic benefits.

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 imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.007
Science and technology studies0.0020.002
Scholarly communication0.0010.002
Open science0.0030.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.336
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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