The effects of advertising models for age‐restricted products and self‐concept discrepancy on advertising outcomes among young adolescents
Bibliographic record
Abstract
Abstract Research on discrepancies between the actual self and ideal self has examined self‐discrepancies in knowledge, skills and stature but age‐based self‐discrepancies have only recently received attention and so we studied this phenomenon in young adolescents. In three studies we identified a product‐category contextual cue that apparently caused adolescents to respond to an existing age‐based self‐discrepancy. Specifically we found that when the contextual cue was advertising for an age‐restricted product, adolescents conformed to dissimilar young adult advertising models and diverged from similar adolescent models. This indicated that the contextual cue caused them to respond to an age‐based self‐discrepancy and use a product associated with the ideal self rather than the actual self. Importantly, this response was stronger among adolescents that were more dissatisfied with their age. With advertising for an age‐unrestricted product, adolescents conformed to adolescent advertising models and diverged from young adult models. Industry policies for age‐restricted products assume that similarity drives influence and therefore mandate that advertising models be young adults rather than adolescents. Our findings suggest this assumption is invalid for age‐restricted products.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".