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Record W3187492257 · doi:10.5210/jbc.v45i1.11643

Dissecting the History of Anatomy in the Third Reich

2021· article· en· W3187492257 on OpenAlexaff
William E. Seidelman

Bibliographic record

VenueJournal of Biocommunication · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicMedical History and Research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAnatomyMedicine

Abstract

fetched live from OpenAlex

This paper is a personal narrative of involvement with the revelations of the use of anatomical and pathological specimens of victims of Nazi terror. The narrative documents responses to the question of the retention and use of anatomical and pathological specimens from victims of Nazi terror by leading academic and scientific institutions and organizations in Germany and Austria including the government of the Federal Republic of (West) Germany, the University of Tübingen, the University of Vienna, the Max Planck Society and the Anatomische Gesellschaft. It begins with the public revelations of 1989 and concludes with the September 2010 Symposium on the History of Anatomy during the Third Reich at the University of Würzburg. The narrative documents a 22-year transition in attitude and responses to the investigation and documentation of the history of anatomy and pathology during the Third Reich. The chronicle includes the 1989 proposed “Call for an International Commemoration” by the author, together with the bioethicist Professor Arthur Caplan, on the occasion of the planned burial of the misbegotten specimens and the responses to that proposal.
 Originally published in Annals of Anatomy Vol. 194, No. 3, 2012© 2011 Elsevier GmbH. All rights reserved.
 Image credit: Table of Contents image provided by the Medical University of Vienna, MUW-AD-003250-5-ABB-90

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.987
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0130.064
Scholarly communication0.0070.006
Open science0.0010.008
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.093
GPT teacher head0.317
Teacher spread0.223 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
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

Citations2
Published2021
Admission routes1
Has abstractyes

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