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Record W1577735324 · doi:10.4471/mcs.2013.26

The Portrayal of Elements Historically Associated with Masculine and Feminine Domains in Lad and Metrosexual Men’s Lifestyle Magazines

2013· article· en· W1577735324 on OpenAlexaff
Rosemary Ricciardelli, Kimberley A. Clow

Bibliographic record

VenueMasculinities & Social Change · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsOntario Tech UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsMasculinityBeautyDietingPsychologyConsumption (sociology)AndrogynyGender studiesSocial psychologySociologyAestheticsArt

Abstract

fetched live from OpenAlex

Differing presentations of masculinity exist that appear to differentially embody elements historically associated with masculine and feminine domains. Metrosexuality, for instance, has been associated with more feminine characteristics and lifestyle choices (Simpson, 1994a) while laddist masculinity was presumed to be more traditionally masculine given its focus on bachelorhood and hedonistic consumption. The present research investigated representations of stereotypical or hyper-masculine (sports, strength, cars) and stereotypical or hyper-feminine (fashion, beauty, dieting) content in a metrosexual and laddist men’s lifestyle magazine. Qualitative and quantitative analyses suggest that the magazines differed in the amount of hyper-masculine material related to sports and strength, but not cars, with laddist magazines portraying this information more than metrosexual magazines. In terms of stereotypical or hyper-feminine material, both laddist and metrosexual magazines depicted fashion frequently, but the metrosexual magazines did portray this information significantly more often. The magazines did not differ in the frequency of portrayals of beauty or dieting; however they did differ in how they portrayed these topics. Implications for masculinities are discussed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.418
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.042
GPT teacher head0.282
Teacher spread0.241 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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