Post-Mortem Pedagogy: A Brief History of the Practice of Anatomical Dissection
Bibliographic record
Abstract
Anatomical dissection is almost ubiquitous in modern medical education, masking a complex history of its practice. Dissection with the express purpose of understanding human anatomy began more than two millennia ago with Herophilus, but was soon after disavowed in the third century BCE. Historical evidence suggests that this position was based on common beliefs that the body must remain whole after death in order to access the afterlife. Anatomical dissection did not resume for almost 1500 years, and in the interim anatomical knowledge was dominated by (often flawed) reports generated through the comparative dissection of animals. When a growing recognition of the utility of anatomical knowledge in clinical medicine ushered human dissection back into vogue, it recommenced in a limited setting almost exclusively allowing for dissection of the bodies of convicted criminals. Ultimately, the ethical problems that this fostered, as well as the increasing demand from medical education for greater volumes of human dissection, shaped new considerations of the body after death. Presently, body bequeathal programs are a popular way in which individuals offer their bodies to medical education after death, suggesting that the once widespread views of dissection as punishment have largely dissipated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".