MétaCan
Menu
Back to cohort
Record W7132731636

Professor Albert Weiss and the Nuremberg trials

2011· article· W7132731636 on OpenAlexaboutno aff
Jelena Đ. Lopičić-Jančić

Bibliographic record

VenueRALF - Repository of the University of Belgrade Faculty of Law · 2011
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicBalkan and Eastern European Studies
Canadian institutionsnot available
Fundersnot available
KeywordsReferentContext (archaeology)Scope (computer science)
DOInot available

Abstract

fetched live from OpenAlex

Autor predstavlja rad prof. dr Alberta Vajsa, člana jugoslovenske delegacije pred Međunarodnim vojnim sudom u Nirnbergu u vremenu od 20. novembra 1945. do 1. oktobra 1946. godine. Vajs je tada bio pravni referent Državne komisije za utvrđivanje zločina okupatora i njihovih pomagača, i kao stručnjak za krivično pravo i poliglota, određen je za člana jugoslovenske delegacije. Vajs je pratio suđenje pred Međunarodnim vojnim sudom u Nirnbergu i sarađivao sa savezničkim tužiocima prilikom predaje naših dokumenata o izvršenim nemačkim ratnim zločinima u Jugoslaviji. O svom radu Vajs je poslao Državnoj komisiji za utvrđivanje zločina okupatora i njihovih pomagača u Beogradu preko 200 izveštaja, predloga, analiza, raznih pisama i dopisa. U ovom tekstu su prvi put u našoj literaturi prikazani deo izveštaja, predloga i analiza Vajsa, na osnovu istraživanja i proučavanja njegovih radova koji se nalaze u Arhivu Jugoslavije i koji su bili potpuno nepoznati našoj naučnoj, stručnoj i najširoj javnosti. Navedeni izveštaji su veoma važni kako s pravnog, tako i s političkog aspekta, za proučavanje toka samog suđenja pred Međunarodnim vojnim sudom u Nirnbergu, jer je Jugoslavija bila saveznička država koja je za vreme Drugog svetskog rata pretrpela ogromne ljudske žrtve i ogromna razaranja. Ubrzo po povratku iz Nirnberga 1947. godine Vajs postaje profesor na Pravnom fakultetu Univerziteta u Beogradu.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.755
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.088
GPT teacher head0.222
Teacher spread0.134 · 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 designTheoretical or conceptual
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
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueRALF - Repository of the University of Belgrade Faculty of LawSame topicBalkan and Eastern European StudiesFrench-language works237,207