Food Advertisements in Two Popular U.S. Parenting Magazines: Results of a Five-Year Analysis
Bibliographic record
Abstract
Obesity rates among American youth have prompted an examination of food advertisements geared towards children. Research indicates children's high exposure to these advertisements and their influence on food preferences. Less is known about the presence of these advertisements in parenting magazines. This study's objective was to examine prevalence of food advertisements in popular parenting magazines and identify products by USDA food category. We analyzed 116 issues of two popular U.S. parenting magazines across five years. All food and beverage advertisements for USDA Food Category were coded. Breakfast cereals were coded for nutritional quality. The coding took place at varied libraries in New Jersey, in the United States. A total of 19,879 food and beverage products were analyzed. One-third of advertisements (32.5%) were for baked goods, snacks, and sweets -- products generally low in nutrient density. Two-thirds of the breakfast cereals were low in nutritional quality (64.6%). Beverages comprised 11% of the advertisements, fruit juices the highest proportion. Less than 3% of advertisements were for fruits and vegetables combined. No significant food product trends were evident across the five-year period. Food advertisements identified in parenting magazines were generally low in nutritional value. Additional research is necessary to determine the influence of food advertisements on parents' purchasing habits.
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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 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".