From body to image—Pernkopf's anatomical gaze and eyewitness accounts on the process of creating images from Nazi victims' bodies
Bibliographic record
Abstract
The Pernkopf atlas is a well-known case study of anatomists' ethical transgressions in using bodies of Nazi victims for professional purposes and the relevance of this history for today. This study examines the likely sources from which Pernkopf developed his own anatomical gaze and pedagogical approach to depicting the human body. It also describes how he inserted himself in the process of creating images from human bodies, including those of executed Nazi victims. Eyewitness accounts allow a reconstruction of the workflow and an understanding of others involved, including morgue technicians, anatomists, work-study students, and illustrators. Also, it appears likely from these accounts that more bodies were needed than the number of 400 images created during the war years suggests, as often several copies of the same dissection, and thus several bodies, were needed for the painting of one image. An analysis of these processes is relevant as Pernkopf was not alone in his use of Nazi victims for anatomical representations. A study of his approach and processes may also shed light on the creation of other 20th century anatomical works from Nazi Germany and its annexed or occupied territories. Notably, the Spanner-Spalteholz atlas has a similar history of ethical transgressions, and the procedural steps identified here for the Pernkopf atlas may inform further studies of the Spanner-Spalteholz history. Going forward, these historical analyses can contribute to the development of history-informed and ethically grounded principles in the creation of innovative anatomical images, especially within emerging new technologies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.027 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".