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Record W2153814552 · doi:10.1071/he08229

Smoking in movies in Australia: who feels over-exposed and what level of regulation will the community accept?

2008· article· en· W2153814552 on OpenAlexaboutno aff
Christine Paul, Raoul A. Walsh, Fiona Stacey, Flora Tzelepis, Wendy Peia Oakes, Anita Tang

Bibliographic record

VenueHealth Promotion Journal of Australia · 2008
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsnot available
Fundersnot available
KeywordsMovie theaterDepictionFilm industryQuarter (Canadian coin)AdvertisingTobacco industryPerceptionCommunity healthPsychologyPopulation healthMedicineEnvironmental healthPublic healthPopulationHistoryNursingBusinessArtVisual arts

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.374
GPT teacher head0.434
Teacher spread0.060 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2008
Admission routes1
Has abstractyes

Explore more

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