Age preferences for advertisement models differ by their gender / Las preferencias de edad de los modelos publicitarios varían en función del sexo del modelo
Bibliographic record
Abstract
Based on evolutionary psychology, this paper investigates whether age preferences for ad models differ according to the model’s gender. The study is the first to experimentally document the double standard of ageing in consumer appraisals of advertisements. Fictitious advertisements were created for mineral water, chewing gum and energy bars, manipulating the ages of the models. Each participant was randomly placed in one of four conditions using a 2 (Model Age) × 2 (Model Gender) between-subjects experimental design and asked to rate the ads. As hypothesized, mature male models elicited a more favourable response than did young ones, whereas mature female models elicited a less favourable response than did young ones. The link between the model’s age and the attitude towards the ad was mediated by the model’s attractiveness. These preliminary results suggest that in choosing ad models, advertisers should take into consideration that mature male models and young female models are rated as attractive, eliciting favourable responses from potential consumers. Nonetheless, mature age can elicit the perception of trustworthiness of the model.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".