Identifying food marketing to teenagers: a scoping review
Bibliographic record
Abstract
BACKGROUND: Teenagers are aggressively targeted by food marketing messages (primarily for unhealthy foods) and susceptible to this messaging due to developmental vulnerabilities and peer-group influence. Yet limited research exists on the exposure and power of food marketing specifically to teenage populations. Research studies often collapse "teenagers" under the umbrella of children or do not recognize the uniqueness of teen-targeted appeals. Child- and teen-targeted marketing strategies are not the same, and this study aims to advance understanding of teen-targeted food marketing by identifying the teen-specific promotion platforms, techniques and indicators detailed in existing literature. METHODS: A systematic scoping review collected all available literature on food marketing/advertising with the term "teenager" or "adolescent" from nine databases, as well as Google Scholar for grey literature, and a hand search of relevant institutional websites. Included were all peer-reviewed journal articles, book chapters, and grey literature in which food marketing to youth was the central topic of the article, of any study type (i.e., original research, reviews, commentaries and reports), and including any part of the 12-17 age range. RESULTS: The 122 articles reviewed define the scope of existing literature on food marketing to young people age 17 and under, identifying leading trends in countries studied (United States, 52%), populations identified (children and teens studied concurrently, 36%), outcomes measured (advertising exposure, 54%), study type (cross-sectional, 58%) and methods used (content analysis, 46%). The promotion platforms and techniques used by food marketers to appeal to young people (as reported in the literature) are also identified and classified. Few studies (7%) use indicators to identify teen-targeted food marketing. CONCLUSIONS: Unique treatments of teen populations are limited in food marketing literature, as is the application of clear indicators to identify and differentiate teen-targeted food marketing from child- or adult-targeted content. Given the need to better measure the presence and power of teen food marketing, this is a significant oversight in existing literature. The indicators identified will help researchers to develop more accurate strategies for researching and monitoring teen-targeted food promotion.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.023 | 0.020 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".