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A Century of Body Snatching: A History of Cadaver‐ Acquisition in Kingston, Ontario from 1820–1920

2013· article· en· W1003656117 on OpenAlexaffabout
Scott Belyea, Jacalyn Duffin, Ron Easteal

Bibliographic record

VenueThe FASEB Journal · 2013
Typearticle
Languageen
FieldMedicine
TopicHistory of Medical Practice
Canadian institutionsQueen's University
Fundersnot available
KeywordsNewspaperLegislationHistoryLawPolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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.

Opus teacher head0.017
GPT teacher head0.239
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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