Online food advertisements and the role of emotions in adolescents’ food choices
Bibliographic record
Abstract
Abstract Adolescence is a critical period for future health outcomes. Food habits and cognitive development are underway, and it is a period of heightened sensitivity to external influences and emotional shifts. We experimentally test the individual and combined influence of food advertisements and emotional primes (i.e., positive, negative, neutral) on adolescent food choices. Participants completed a food choice task selecting five snacks out of twenty healthy and unhealthy options in an online experiment. Prior to the food choice, we randomized whether adolescents were exposed to unhealthy food or non‐food online advertisements. To induce experimental variation in adolescents’ emotions, they were assigned to watch two, two‐minute film clips validated to elicit the targeted emotion. The online food advertisement did not significantly impact food choices, except that Black and Hispanic groups selected a higher share of calories from unhealthy foods. Participants in a negative emotional state selected more unhealthy sweet snacks. Finally, we find only weak evidence that a positive emotional state amplified the impact of food advertisements on the nutritional quality of food selection. Together, results suggest that while a negative emotional state drives food choices, this pattern occurs independently from food advertisement exposure.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".