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Record W2015484023 · doi:10.1037/1524-9220.8.3.145

Media images of men: Trends and consequences of body conceptualization.

2007· article· en· W2015484023 on OpenAlexafffund
Jamie C. Farquhar, Louise Wasylkiw

Bibliographic record

VenuePsychology of Men & Masculinity · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsMount Allison University
FundersMount Allison University
KeywordsConceptualizationPsychologyMedia contentSocial psychologyMultimediaComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

In the current investigation, the authors examined how men are presented in popular media and the effects of such presentations on male adolescents’ self-evaluations. In a content analysis of male models in advertisements of Sports Illustrated, Study 1 showed that media ideals increasingly emphasize aesthetic versus performance attributes of men. In Study 2, male adolescents (N 107) were randomly assigned to view either images of male ideals emphasizing aesthetic attributes, images of male ideals emphasizing performance attributes, or neutral images. Results showed that viewing media ideals that emphasize aesthetic attributes contributes to negative selfevaluations whereas viewing media ideals that emphasize performance attributes contributes to positive self-evaluations. These findings suggest that body conceptualization, and not simply body type (i.e., muscularity), plays a role in how men feel about themselves and their bodies.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.049
GPT teacher head0.391
Teacher spread0.342 · 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 source (direct Gemma or distilled Codex), 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

Citations99
Published2007
Admission routes2
Has abstractyes

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