Morphing Bodies and Mechanicial Eyes: A Reassessment of the Photographic Iconography of the Salpêtrière
Bibliographic record
Abstract
Photography’s unique ability to render visible what is ordinarily invisible lends it to scientific observation and artistic expression alike. Jean-Martin Charcot and his colleagues exploit this capacity of the photograph in their documentation of hysteria—work that is located at a blurry intersection between art and science. The photographs taken at the Salpêtrière women’s asylum in Paris, France, for their original publication in La Nouvelle Iconographie de la Salpêtrière (1881), came to my attention through Georges Didi-Huberman’s book, the Invention of Hysteria (2003). They depict, through a clinical eye, patients that are mid-gesture, in the throes of hysterical fits. The hysteria documents exemplify an instance where the power of the performed gesture is focalized by doctors in the production of identities, and where the power of the photographic apparatus is deployed in the production of gestures. I intend to show how the body and its gestures might have been understood in the field of neuropsychiatry at the time these photographs were exposed, and then I will look at the means by which machines produce gestures with some degree of autonomy. Given a new understanding of the independent functions of imaging technologies, it will be possible to argue that there are some manners in which the technologies of representation actually participate in the production of bodies.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.009 | 0.044 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".