Data on gender representation in food and beverage print advertisements found in corner stores from Guatemala and Peru
Bibliographic record
Abstract
OBJECTIVES: Data on gender representation in food and beverage advertisements may allow for a better understanding of how the food industry is targeting different audiences based on gender. Nonetheless, scant research on food and beverage print advertising with a gender approach has been conducted. Therefore, we sought to assess the prevalence of gender focus in print advertisements found inside corner stores in two cities: Guatemala City, Guatemala, and Lima, Peru. DATA DESCRIPTION: We developed two complementary datasets as part of the study: (1) a dataset of digital photographs of 200 food and beverage print advertisements found in corner stores located near schools (100 ads per country selected according to criteria such as product type, image quality, and uniqueness); (2) a quantitative dataset with data of the content analysis of these photographs. We employed 19 variables to record the general information and gender assessment of the ads. These datasets should allow scholars and public officials to identify gender-specific marketing strategies of the food industry that might impact children's and adolescents' nutrition differently.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".