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Record W4242670869 · doi:10.32920/ryerson.14647071

The Semi-Nude and Collapsing Female Figure: Sexuality in the Aesthetic Paintings of Albert Moore and Edward Burne-Jones

2021· preprint· en· W4242670869 on OpenAlexaff
Meaghan Collins

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicFashion and Cultural Textiles
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsPaintingHuman sexualityArtMeaning (existential)Period (music)Order (exchange)Art historyAestheticsGender studiesPsychologySociology

Abstract

fetched live from OpenAlex

The female nude has been researched extensively over the history of Art, and continues to fascinate researchers and art enthusiasts alike. However it is difficult to find information on female figures who are semi-nude: one who is not fully clothed nor entirely nude. The Victorian period in England was a very conservative time, especially for women. Influenced by the Pre-Raphaelites, Aesthetic painters Albert Moore and Edward Burne-Jones depicted women in a peculiarly sexual manner, one that encapsulates the struggle with sexual acceptance of the era. Women are often shown wearing loose, transparent fabric and often asleep or in languid, weary poses. These weary poses are what I refer to as “collapsing women.” In this visual analysis I take a close look at these female figures, and examine them using the terms “semi-nude” and “collapsing female,” in order to determine the meaning behind their dress and body language.

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.010
Scholarly communication0.0040.001
Open science0.0000.002
Research integrity0.0010.002
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 head0.034
GPT teacher head0.254
Teacher spread0.219 · 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
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
Published2021
Admission routes1
Has abstractyes

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