Figuration et esthétique de l’identité génétique : autour de l’Autoportrait génétique de Gary Schneider
Bibliographic record
Abstract
This article considers the ways artists interpret the phenomena of genetic identity through portraiture. After a survey of genetic iconography, it concentrates on the Genetic Self-Portrait (1997) of Gary Schneider. This portrait contains fifty-five black-and-white photographs showing Schneider's own chromosomes, DNA, and several highly individualized body parts such as hand, ear, iris, and sperm. Most of these fragments have been registered with technological medical devices. Critics have underlined the sublime in the genetic aesthetic carried in Schneider's prints. They also have acknowledged the recurrence of mythic and religious beliefs in the genetic aesthetic. Moreover, considering genetic identity, critics have raised the issues of predicting one’s biological future and of tracing one’s ethnic origin. The author argues that Schneider’s Genetic Self-Portrait pushes the logic of the ID portrait paradigm by replacing the face with genetic identifiers and by maintaining the scientific quality of photography. It is also shown that the genetic portrait entails a “double-bind” relationship because it involves a contradiction between the expected resemblance of portraiture and the genetic features in which the model cannot recognize himself. But in return, in Schneider's prints, this portrait-with-out-face offers associative and narrative possibilities that solve the paradox. The rhetoric of Schneider’s self-portrait alludes to an already known conception of the human being in a cosmological view that emphasizes sexuality and hereditary transmission. The genetic portraits of Kevin Clarke, Inigo Manglano-Ovalle, and Marc Quinn are also considered for their different approaches to genetic identity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".