“Drinking Won't Get You Thinking”: A Content Analysis of Adolescent-Created Print Alcohol Counter-advertisements
Bibliographic record
Abstract
Involvement in creating antialcohol advertisements generates enthusiasm among adolescents; however, little is known about the messages adolescents develop for these activities. In this article, we present a content analysis of 72 print alcohol counteradvertisements created by high school (age 14-17 years old) and college (18-25 years old) students. The posters were content analyzed for poster message content, persuasion strategies, and production components, and we compared high school and college student posters. All of the posters used a slogan to highlight the main point/message of the ad and counterarguments/consequences to support the slogans. The most frequently depicted consequences were negative consequences of alcohol use, followed by negative-positive consequence comparison. Persuasion strategies were sparingly used in advertisements and included having fun/one of the gang, humor/unexpected, glamour/sex appeal, and endorsement. Finally, posters displayed a number of production techniques including depicting people, clear setting, multiple colors, different font sizes, and object placement. College and high school student-constructed posters were similar on many features (e.g., posters displayed similar frequency of utilization of slogans, negative consequences, and positive-negative consequence comparisons), but were different on the use of positive consequences of not using alcohol and before-after comparisons. Implications for teaching media literacy and involving adolescents and youth in developing alcohol prevention messages are discussed.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".