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Record W2040883915 · doi:10.3389/fpubh.2013.00002

Alcohol-Related Content of Animated Cartoons: An Historical Perspective

2013· article· en· W2040883915 on OpenAlexaff
Hugh Klein, Kenneth S. Shiffman

Bibliographic record

VenueFrontiers in Public Health · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsKensington Health
Fundersnot available
KeywordsPerspective (graphical)Public healthContent (measure theory)Front (military)Public health interventionsComputer scienceMedicineNursingArtificial intelligenceGeography

Abstract

fetched live from OpenAlex

This study, based on a stratified (by decade of production) random sample of 1,221 animated cartoons and 4,201 characters appearing in those cartoons, seeks to determine the prevalence of alcohol-related content; how, if at all, the prevalence changed between 1930 and 1996 (the years spanned by this research); and the types of messages that animated cartoons convey about beverage alcohol and drinking in terms of the characteristics that are associated with alcohol use, the contexts in which alcohol is used in cartoons, and the reasons why cartoon characters purportedly consume alcohol. Approximately 1 cartoon in 11 was found to contain alcohol-related content, indicating that the average child or adolescent viewer is exposed to approximately 24 alcohol-related messages each week just from the cartoons that he/she watches. Data indicated that the prevalence of alcohol-related content declined significantly over the years. Quite often, alcohol consumption was shown to result in no effects whatsoever for the drinker, and alcohol use often occurred when characters were alone. Overall, mixed, ambivalent messages were provided about drinking and the types of characters that did/not consume alcoholic beverages.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.136
GPT teacher head0.352
Teacher spread0.215 · 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 designQualitative
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
Published2013
Admission routes1
Has abstractyes

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