Gender representation in food and beverage print advertisements found in corner stores around schools in Peru and Guatemala
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to assess gender representation in food and beverage print advertisements. RESULTS: The study follows a quantitative descriptive approach. Using a content analysis technique, we assessed the gender representation in 200 food and beverage print advertisements found in corner stores located in four areas around schools in Lima, Peru, and Guatemala City, Guatemala (100 advertisements per country). A total of 36% of the print advertisements exhibited a male main character for the case of Guatemala, while in Peru 14% of the print advertisements presented a male main character. Furthermore, in Guatemala, 22% of the main characters were male animated characters. Moreover, 27% of the print advertisements in Guatemala and 17%, in Peru were visually male-oriented. Overall, male characters appeared alongside sports references and in varied settings, whereas female characters were usually holding or consuming the product. In conclusion, although the majority of variables used to assess the representation of gender in food and beverage print advertisements were gender-neutral, those showing gender representation were mostly male-oriented. Despite its limited findings, the study provides evidence for the formulation of public policies and educational content aimed to protect children's and adolescents' health from the effects of food marketing.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".