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Record W66362790 · doi:10.4000/ifha.1540

BASTIAN, Till, Furchtbare Aerzte. Medizinische Verbrechen im Dritten Reich

2013· article· de· W66362790 on OpenAlexaff
Isabelle von Bueltzingsloewen

Bibliographic record

VenueRevue de l’Institut français d’histoire en Allemagne · 2013
Typearticle
Languagede
FieldArts and Humanities
TopicMedical History and Research
Canadian institutionsInstitut d'Histoire de l'Amérique Française
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Les crimes perpétrés par les médecins allemands à l’époque nazie ont, après de longues années de silence, suscité une multitude d’enquêtes menées par des historiens et par des médecins soucieux d’assumer l’héritage de leurs pères. Ce petit ouvrage publié dans la collection Beck’sche Reihe, rédigé par un spécialiste du nazisme, n’apporte pas de »révélation« sur ce sujet particulièrement sensible mais propose à un large public, dans un style d’une sobriété très pédagogique, une bonne synthèse ...

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)0.001
metaresearch head score (Gemma)0.002
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.011
Scholarly communication0.0040.003
Open science0.0000.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0100.002

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 head0.022
GPT teacher head0.237
Teacher spread0.215 · 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 designNot applicable
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 routes1
Has abstractyes

Explore more

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