Food Marketing and Power: Teen-Identified Indicators of Targeted Food Marketing
Bibliographic record
Abstract
Food marketing is powerful and prevalent, influencing young people's food attitudes, preferences, and dietary habits. Teenagers are aggressively targeted by unhealthy food marketing messages across a range of platforms, prompting recognition of the need to monitor such marketing. To monitor, criteria for what counts as teen-targeted food marketing content (i.e., persuasive techniques) must first be established. This exploratory study engaged teenagers to explore the "power" of food marketing by identifying what they consider to be teen-targeted marketing techniques within various food marketing examples. Fifty-four teenagers (ages 13-17) participated in a tagging exercise of 19 pre-selected food/beverage advertisements. Assessed in light of age and gender, the results showed clear consistency with what indicators the participants identified when it comes to selecting "teen-targeted" ads-with advertisements most frequently chosen as "teen-targeted" containing humor (particularly irony) and celebrities. When it comes to specific indicators used by teenagers, visual style dominated, standing as the marketing technique with the most "power" for teenagers. The findings shed much needed insight into the elements of power-and more precisely, the specific marketing techniques persuasive to teenagers-which are necessary to inform monitoring efforts and to create evidence-based policy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".