Quantifying Child-Appeal: The Development and Mixed-Methods Validation of a Methodology for Evaluating Child-Appealing Marketing on Product Packaging
Bibliographic record
Abstract
There is no standardized or validated definition or measure of “child-appeal” used in food and beverage marketing policy or research, which can result in heterogeneous outcomes. Therefore, this pilot study aimed to develop and validate the child-appealing packaging (CAP) coding tool, which measures the presence, type, and power of child-appealing marketing on food packaging based on the marketing techniques displayed. Children (n = 15) participated in a mixed-methods validation study comprising a binary classification (child-appealing packaging? Yes/No) and ranking (order of preference/marketing power) activity using mock breakfast cereal packages (quantitative) and focus group discussions (qualitative). The percent agreement, Cohen’s Kappa statistic, Spearman’s Rank correlation, and cross-classification analyses tested the agreement between children’s and the CAP tool’s evaluation of packages’ child-appeal and marketing power (criterion validity) and the content analysis tested the relevance of the CAP marketing techniques (content validity). There was an 80% agreement, and “moderate” pairwise agreement (κ [95% CI]: 0.54 [0.35, 0.73]) between children/CAP binary classifications and “strong” correlation (rs [95% CI]: 0.78 [0.63, 0.89]) between children/CAP rankings of packages, with 71.1% of packages ranked in the exact agreement. The marketing techniques included in the CAP tool corresponded to those children found pertinent. Pilot results suggest the criterion/content validity of the CAP tool for measuring child-appealing marketing on packaging in accordance with children’s preferences.
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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 | 0.305 | 0.304 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 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".