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Record W2158550474 · doi:10.1177/1097184x13502662

“Dere’s Not Just One Kind of Fat”

2013· article· en· W2158550474 on OpenAlexaff
Moss E. Norman

Bibliographic record

VenueMen and Masculinities · 2013
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsEmbodied cognitionPrivilege (computing)OppressionMasculinityContext (archaeology)SociologyGender studiesPower (physics)Sociocultural evolutionBeautyAestheticsPoliticsBiologyEpistemologyAnthropology

Abstract

fetched live from OpenAlex

In the current sociopolitical context, the lean, muscular body has come to epitomize masculine health and beauty. Not all boys and young men, however, unequivocally subscribe to dominant constructions that position fatness as unhealthy and unattractive. Using qualitative inquiry with thirty-two “skinny” or “normal”-bodied young men (thirteen to fifteen years of age), I demonstrate that fat talk is a prominent resource through which “normal” masculine embodiment is achieved. More specifically, I reveal that sociocultural positioning influences how young men take up, make sense of, and articulate constructions of fatness and demonstrate how such articulations function in the materialization of their “normal” embodied subjectivities. I also examine how fat masculinities operate differently within diverse emplaced contexts and in relation to distinct discursive communities. Such a line of investigation I argue helps to reveal the ways in which power relations of privilege and oppression are performatively embodied in everyday contexts.

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)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0030.017
Scholarly communication0.0030.003
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.161
GPT teacher head0.426
Teacher spread0.265 · 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

Citations17
Published2013
Admission routes1
Has abstractyes

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