Smoking in movies in Australia: who feels over-exposed and what level of regulation will the community accept?
Bibliographic record
Abstract
OBJECTIVE: This study aimed to examine recent levels of exposure to smoking in movies, how the community perceived the level of smoking they saw in recently-viewed movies and whether there was community support for any form of regulation. METHODS: As part of a 2004 New South Wales survey of smoking-related perceptions and practices, 1,154 adults participated in a computer-assisted telephone interview about perceptions relating to smoking depiction in movies and television. RESULTS: More than one-quarter of those who had seen a recent movie in the cinema (28.5%) or on DVD (33.9%) thought that the movie contained excessive or inappropriate smoking. More than half the sample (59.1%) considered it likely the tobacco industry played a role in the level of smoking depiction, although only 18% of those who thought a recent movie contained excessive smoking attributed this to the tobacco industry. Almost two-thirds of respondents favoured screening anti-tobacco advertisements prior to movies with smoking. CONCLUSION: Cinema and DVD movies commonly include scenes where there is excessive or inappropriate smoking. It is widely believed that the tobacco industry is contributing to this, and there is strong community support for action to curb the harmful influences this may be having.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".