Alcohol-Related Content of Animated Cartoons: An Historical Perspective
Bibliographic record
Abstract
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".