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Record W1923601997 · doi:10.32396/usurj.v1i2.89

Men’s Construction of Media Impact on Male Body Image in the Context of Heterosexual Romantic Relationships

2015· article· en· W1923601997 on OpenAlexaffvenue
Meaghan Beck Baker, Kurtis John Allen, Qingqi Thomas Qiao

Bibliographic record

VenueUSURJ University of Saskatchewan Undergraduate Research Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsConstruct (python library)RomancePsychologyContext (archaeology)Social psychologyNegotiationMeaning (existential)Focus groupHeterosexualityQualitative researchHuman sexualityGender studiesDevelopmental psychologySociology

Abstract

fetched live from OpenAlex

Research has shown that men, like women, are negatively affected by idealized media portrayals. However, there are few qualitative studies that have examined this issue, leaving the literature impoverished of the meaning of male body image to men. Specifically, there is a lack of discursive inquiry. Since romantic relationships provide a unique context for body image construction, the current study examines the ways in which men in relationships linguistically construct and negotiate the media’s influence on their own body image. Three interviews and a focus group of two men in heterosexual relationships were employed to explore this topic. Our results show that, while men speak of a negative influence of media on their body image, they do so in such a way that is adaptive and creates protective barriers between the self and the media’s messages. These strategies, which at times utilize relationship status, allow participants to construct general, albeit sometimes reluctant, satisfaction with their bodies. These findings, while largely concurring with previous literature, add caveats and may reconcile disparate results from past research by identifying contradictory constructions of media influence on males in heterosexual relationships.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.096
GPT teacher head0.348
Teacher spread0.252 · 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 designQualitative
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

Citations0
Published2015
Admission routes2
Has abstractyes

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