California film subsidies and on-screen smoking: Resolving the policy conflict
Bibliographic record
Abstract
Cumulative exposure to on-screen smoking is a major recruiter of new young smokers. Policy solutions—including R-rating films with tobacco imagery and making productions with tobacco ineligible for public subsidies—are backed by health authorities in California and worldwide. Exposure to on-screen smoking accounts for nearly 100,000 current smokers in California aged 12-17. Total costs of medical services for this group, through age 50, are estimated at $1.6 billion (discounted present value). Two-thirds of the cost will be borne by government. Top-grossing films made in California accounted for one-third of United States audience exposure to on-screen tobacco imagery 2002-11. From mid-2009 through 2011, California approved $374 million in film and television production subsidies, in the form of tax credits. $128 million was approved for 27 feature films, released widely 2010-11, that achieved top-grossing status. Sixteen of these films featured tobacco imagery; $75 million was approved for these films, which made $1.1 billion at the box office. More than two-thirds ($51 million) of California tax credits approved for top-grossing films with tobacco imagery went to PG-13 films. Nearly 80 percent (2 billion/2.5 billion) of in-theater tobacco impressions delivered in the US and Canada by California-subsidized, top-grossing films came from films rated PG-13. (The rest came from R-rated films.) Tobacco content of top-grossing films varies by company. Forty-four percent of California subsidies approved for top-grossing feature were reserved for films released by Sony and Viacom (Paramount). Films from these two companies garnered 71 percent of California subsidies for films with tobacco and 83 percent of subsidies for youth-rated (PG-13) films with tobacco. Of the $1.6 billion in costs of direct medical services that will be incurred for teen smokers in California recruited by their exposure to films with tobacco imagery, $510 million is attributable to adolescents’ exposure to films made in California. If the California film subsidy program continues and the pattern of subsidies and smoking films remains the same as in the past, films containing tobacco and subsidized by California taxpayers will contribute an estimated 17,000 new 12-17 year old smokers among the next cohort of 12-17 year old smokers in California, who will incur an estimated $270 million in smoking-induced costs. Public health authorities, including the US Centers for Disease Control and Prevention, the World Health Organization, the director of Los Angeles County’s Department of Public Health, and the chair of California’s Tobacco Education and Research Oversight Committee (TEROC) have highlighted the policy contradiction between state subsidies for films with tobacco imagery and state tobacco prevention programs. The policy solution is to amend the California tax credit program statute, adding the following to the existing list of productions disqualified from eligibility for subsidy: …any production that depicts or refers to any tobacco product or non-pharmaceutical nicotine delivery device or its use, associated paraphernalia or related trademarks or promotional material. Such a change would end the practice of taxpayers paying for commercial films with tobacco imagery that subvert the important public goal of reducing youth smoking and its consequent health costs, many of which are borne by the public.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.020 | 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".