Development of a Teen-Informed Coding Tool to Measure the Power of Food Advertisements
Bibliographic record
Abstract
The food-related information environment, comprised of food and beverage advertising within one's surroundings, is a growing concern for adolescent health given that food marketing disproportionately targets adolescents. Despite strong public interest concerning the effects of food marketing on child health, there is limited evidence focused on outdoor food advertising in relation to teenage diets, food purchasing, and perceptions. Further, limited research has considered both the exposure to and influence of such advertisements. This study used a novel multi-method approach to identify and quantify the features of outdoor food and beverage advertisements that are most effective at drawing teenagers into retail food establishments. An environmental audit of outdoor advertisements and consultations with youth were used to: (1) identify teen-directed food marketing techniques; (2) validate and weigh the power of individual advertising elements; and, (3) develop a teen-informed coding tool to measure the power of food-related advertisements. Results indicate that marketing power is a function of the presence and size of teen-directed advertisement features, and the relative nature of each feature is an important consideration. This study offers a quantitative measurement tool for food environment research and urges policymakers to consider teen-directed marketing when creating healthy communities.
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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 | 0.035 | 0.097 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".