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Record W4400823497 · doi:10.32920/jcd.v7i2.2052

“I Just Want to Be Normal!”

2024· article· en· W4400823497 on OpenAlexaffvenue
Adam Davies

Bibliographic record

VenueJournal of Critical Dietetics · 2024
Typearticle
Languageen
FieldHealth Professions
TopicObesity and Health Practices
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

In this article, I explore both my personal experiences within queer men’s communities engaging with dieting practices and my journey encountering assessments for gynecomastia. Gynecomastia, a medical condition that typically describes cisgender men’s experiences with enlarged breasts and extra fat tissue on their pectorals, is an under-discussed medical condition that intersects with conversations around gender, sexuality, body image and self-esteem. Within queer men’s communities, the hegemonic emphasis on toned, ‘fit’ bodies and muscular pectoral muscles leaves individuals with gynecomastia with body image and self-esteem challenges. Accordingly, hierarchical notions of queer masculinities often position feminine embodiment, particularly that which is associated with fat and fleshiness, as lesser than. In this poststructural autoethnographic article, I engage with my personal experiences of being assessed for gynecomastia and personal histories with body image challenges. Through this, I describe the hegemonic norms within queer men’s communities that constitute feminized and fattened embodiments as inferior and place my personal experiences with body image and self-esteem challenges within a larger socio-cultural and political landscape that devalues bodies outside of hegemonic and masculinized norms. I provide implications for dietetic professional practices throughout the article and advocate for an awareness of social and cultural norms around gender and sexuality in dietetic professional practices.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.899
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.002

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 head0.178
GPT teacher head0.552
Teacher spread0.375 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations4
Published2024
Admission routes2
Has abstractyes

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