A Century of Body Snatching: A History of Cadaver‐ Acquisition in Kingston, Ontario from 1820–1920
Bibliographic record
Abstract
Practical anatomy has always required bodies for dissection, and, when legal avenues failed to supply cadavers, more illicit practices developed. Scholarly and popular works on body snatching have addressed grave robbing in the United Kingdom and North America. The rise of American medical schools and their reliance on grave robbing have been well‐documented through substantial works by Suzanne Shultz and Michael Sappol, while the Canadian landscape has yet to be researched as thoroughly. This work begins to fill the gap by focussing on grave robbing in late nineteenth‐century Eastern Ontario. Lying between Toronto, and Montreal, and in close proximity to the American border, the area surrounds Kingston's medical school, founded in 1854. Sources include newspapers, pertinent legislation, and archival records of the Medical Faculty and hospital. They identify dozens of local body‐snatching cases with an uneven distribution through time, and they trace the public and medical reactions to grave robbing for anatomical study. Factors included the shortcomings of legal avenues of cadaver‐acquisition, unclaimed remains in the Kingston Penitentiary, and the city's cemetery practices. A more complete view emerges of early Canadian medical life and a reconsideration of the previously‐suggested grave‐robbing landscape in Canada with its North American implications.
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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 | 0.003 | 0.005 |
| Science and technology studies | 0.014 | 0.017 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".