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Record W3178077222 · doi:10.1186/s13104-021-05812-4

Gender representation in food and beverage print advertisements found in corner stores around schools in Peru and Guatemala

2021· article· en· W3178077222 on OpenAlexfundno aff
Sophia Mus, Lucila Rozas, Joaquín Barnoya, Peter Busse

Bibliographic record

VenueBMC Research Notes · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
FundersInternational Development Research CentreWellcome Trust
KeywordsAdvertisingRepresentation (politics)MedicineGeographyEnvironmental healthBusinessPolitical science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.343
GPT teacher head0.477
Teacher spread0.135 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes1
Has abstractyes

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