Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.067 | 0.210 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.006 |
| Insufficient payload (model declined to judge) | 0.069 | 0.016 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".