Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".