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Record W4391263170 · doi:10.5206/tba.v5i1.16558

From Plastic Surgery to National Identity.

2024· article· en· W4391263170 on OpenAlexvenueaboutno aff
Philip Gurrey

Bibliographic record

Venuetba Journal of Art Media and Visual Culture · 2024
Typearticle
Languageen
FieldMedicine
TopicDigital Imaging in Medicine
Canadian institutionsnot available
Fundersnot available
KeywordsPlastic surgeryGeneral surgeryMedicineSurgery

Abstract

fetched live from OpenAlex

British surgical photographer Percy Hennell (1911-1987) worked alongside the ‘world famous … father of modern plastic surgery’[1] Harold Delf Gilles (1882-1960) to capture pioneering reconstruction facial surgery in 1941-42. The two toured America and Canada during this time helping to bolster ‘the status of Britain at war at a time when, initially, America was still teetering between neutrality and entering the conflict.’[2]
 The works shown here further dissect Hennell’s photographs by juxtaposing a number of his subjects into new painted images. This piecing together of images in paint hints at the now common use of plastic surgery in modern society. It marks the shift away from reconstruction and reassimilation towards vanity and the meddling in our ‘own physical attributes in a desire to sculpt a vision of perfect beauty.’[3]
 Manipulation through image connects painting to surgical operations, these works also draw on ideas of image, beauty, state propaganda and war. A direct reference to Eisenstein’s film “Battleship Potemkin” can be found in the glasses of one particular subject.
 
 [1] Christine Slobogin, “Full Article: ‘Something Useful in a National Sense’: Percy Hennell’s Surgical and Nationalist Colour Photography, 1940-1948,” Taylor and Francis online, November 8, 2022, https://www.tandfonline.com/doi/full/10.1080/14714787.2022.2094458.
 [2]Slobogin, 2022.
 [3] Madder139 Gallery. “Philip Gurrey.” Madder139 press release, May 15, 2008. www.madder139.com.

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)0.000
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.351
Teacher spread0.326 · 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 teacher head, 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
Published2024
Admission routes2
Has abstractyes

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