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Record W3106808666 · doi:10.38055/fs030101

Body As Architecture: Designing the Stout Body in the Age of Standardization

2020· article· en· W3106808666 on OpenAlexvenueno aff
Lauren Downing Peters

Bibliographic record

VenueFashion Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsnot available
Fundersnot available
KeywordsArchitectureGestalt psychologyStyle (visual arts)AestheticsSociologyEpistemologyLiteratureArtVisual artsPhilosophy

Abstract

fetched live from OpenAlex

This article explores the curious intersections of stoutwear design, Gestalt Psychology, and architectural discourse in early twentieth-century American fashion media. In doing so, it focuses principally on trade media, style guides and advertisements that grappled with the perceived flaws of the stout woman’s physique and how sophisticated design principles, if properly handled, could create the appearance of bodily slenderness. By moving beyond the biological determinism of contemporary obesity discourse, this article argues that ideas about stoutness and, more specifically, what constituted a stout body, were produced through attempts to contain, control, and correct the fat, female body in fashion design discourse. By further embedding this research within a broader consideration of the relationship between bodies, dress, architecture, and modernist design thinking, this article argues that the mediums and discourses of fashion can open up pathways for thinking about the body itself as “designed.”

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)0.004
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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.027
Scholarly communication0.0080.007
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.090
GPT teacher head0.296
Teacher spread0.206 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2020
Admission routes1
Has abstractyes

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