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Record W4362560008 · doi:10.4000/criminocorpus.12776

« Le dernier procès allemand pour crimes de guerre » : L'ancien officier SS Julius Viel et le procès de Ravensburg d’avril 2001 comme événement médiatique

2023· article· fr· W4362560008 on OpenAlexaboutno aff
Vojtěch Kyncl

Bibliographic record

VenueCriminocorpus revue hypermédia · 2023
Typearticle
Languagefr
FieldSocial Sciences
TopicCommunism, Protests, Social Movements
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

Au cours de la seconde moitié du vingtième siècle, les médias ont joué un rôle important dans la couverture des procès des auteurs des crimes nazis. Indépendamment du nombre de victimes, les procès de criminels nazis sont devenus des événements médiatiques qui sortaient du cadre habituel des reportages sur la juste punition des crimes violents. Le « dernier » procès allemand de 2001 à Ravensburg, nom donné au procès de l'assassin de sept prisonniers juifs près de Litoměřice/ Leitmeritz en 1945, est-il devenu un événement médiatique qui a clôturé toute une phase de travail sur les crimes nazis dans la société euro-atlantique ? Quelles informations sur le crime et son contexte historique, sur l'auteur du crime, les témoins-clés SS et le procès lui-même la couverture médiatique des principaux journaux du monde offrait-elle au grand public ? Dans quelle mesure correspondait-elle aux représentations existantes et aux connaissances sur les auteurs des crimes nazis, leurs actes et la vie après la guerre dans les sociétés tchèque, allemande et canadienne ?

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.072
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.012
Scholarly communication0.0100.005
Open science0.0010.005
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.144
GPT teacher head0.358
Teacher spread0.214 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2023
Admission routes1
Has abstractyes

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