Down the Rabbit Hole: The Madness of State Film Incentives as a 'Solution' to Runaway Production
Bibliographic record
Abstract
This article is a sequel to my first law review article on runaway productions called Through the Looking Glass: Runaway Productions and Hollywood Economics, published in The University of Pennsylvania Journal of Labor and Employment Law in August 2007. Since 2007, there has been a race to the bottom as virtually every state has enacted significant, if not detrimentally generous, tax incentives to lure film and television production. The efficacy of these incentives is evaluated at length, with particular attention paid to the origin and implementation of tax incentives in California, Massachusetts and Louisiana - states with colorful backgrounds on this issue. The paper suggests that the current solution to the runaway production problem (competing state incentives) is counter-productive to the point of becoming the problem and calls for the enactment of a single national tax incentive for the entire nation to better compete with foreign production locales like Canada.
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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 | 0.001 | 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 | 0.001 |
| 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".