Bibliographic record
Abstract
BACKGROUND: Multiple studies have indicated that the Joe Camel advertising campaign has been successful in marketing tobacco to children and adolescents, whereas other studies have reported that current tobacco warning messages are ineffective. OBJECTIVE: To determine the importance and believability of familiar and novel tobacco warning messages with and without cartoons that were modeled after Joe Camel. DESIGN: Children and adolescents (N = 580) in Chicago, Ill, public schools were surveyed to determine the believability and importance of 3 cartoon tobacco warnings modeled after Joe Camel developed with the messages "Smoking Causes Lung Cancer, Heart Disease, Emphysema, and May Complicate Pregnancy" or "Smoking Kills" and the same 2 messages without cartoons. RESULTS: Respondents rated all 3 cartoons significantly more believable than the plain condition regardless of the message (P<.05). Furthermore, respondents rated the "Smoking Causes Lung Cancer, Heart Disease, Emphysema, and May Complicate Pregnancy" warning significantly more believable and important than the "Smoking Kills" message across all 4 cartoon conditions (walrus, penguin, bear, and no cartoon) (P<.01). Selected demographic groups found particular cartoon and warning messages more believable and/or important than others. CONCLUSIONS: The finding that cartoon tobacco warnings are more believable than plain warnings suggests that it may be desirable to include cartoons in future tobacco warning labels. The lower ratings of believability and importance of the "Smoking Kills" warning is a concern because similar warnings have recently been implemented in at least 2 countries (Australia and Canada) and have been considered for implementation in the United States. Arch Pediatr Adolesc Med. 2000;154:1230-1236.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.010 | 0.001 |
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".