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Record W2049515211 · doi:10.3149/jmh.0502.109

Male Voices on Body Image

2006· article· en· W2049515211 on OpenAlexaff
Gina Bottamini, Diane M. Ste‐Marie

Bibliographic record

VenueInternational Journal of Men s Health · 2006
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsImage (mathematics)PsychologyCommunicationComputer visionComputer science

Abstract

fetched live from OpenAlex

This study examines male body image perceptions, motivations, and related behaviors through a qualitative approach. Eleven males between the ages of 18 and 25 participated in two semistructured interviews. During the first interview, participants were presented with three pictures that represented different body types as well as two image scales that ranged in muscularity and adiposity. The images served to stimulate conversation about a number of questions posed. The follow-up interviews permitted the participants to voice additional information pertaining to the first interview as well as assess their comfort level with the interview process. Discussion surrounds the males’ level of body satisfaction, perception of the ideal male physique, perceived influences, perceived psychosocial consequences, perceived motivations, and behaviors employed to attain or maintain their desired body type. Finally, the utilization of cross-gender interviewing when investigating male body image is addressed.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.017
GPT teacher head0.388
Teacher spread0.371 · 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 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

Citations58
Published2006
Admission routes1
Has abstractyes

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