Bibliographic record
Abstract
Body Worlds is a hugely popular exhibition that claims to offer a reverential and educational experience of the ‘real human body’ through the display of plastinated dead human bodies. However, because they are posed, staged, and composed of significant nonhuman artifice, plastinates are ambivalently ‘real’ as human bodies, let alone ‘real’ as humans. Plastinates are as much nonhuman as human, and neither category fully accounts for them. In this article, I discuss the consequences of this for feminist theory. Approaches in feminist theory that reify, either implicitly or explicitly, a human/nonhuman binary framework are challenged by plastinates. I show that locating plastinates within either ontological category, though not fully accounting for them, enables feminist critiques of the exhibition; however, these categories also paradoxically permit forms of violence with which feminists are typically concerned. In this way, I argue that plastinates force feminist thought to the very interface of the human/nonhuman divide. When applied to Body Worlds, these concepts at best form a heuristic ontological hinge whose angle is determined by ethical and political commitments, illustrating the ways in which key ontologies should be seen as political strategies more or less amenable to feminist goals, but not more or less true. I argue that what lies at the crux of this hinge, in the case of plastinates, is death, and suggest that Body Worlds demands that the interface of death with life become a key feminist concern.
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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 | 0.002 | 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 | 0.005 | 0.064 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".