The Problem of Nazi Criminals and Their Accomplices Punishment in the Context of the Great Patriotic War Memory Preservation. Approaches, Examples and Prospects.
Bibliographic record
Abstract
The results of the vote in the UN General Assembly in December 2024 on the resolution regarding the struggle against Nazism glorification showed that among those traditionally opposing countries were those that for many years demonstrated low or no effectiveness in punishing Nazi collaborators. Examples of Nazi criminals impunity confirm that in Canada, the USA and Western countries their persecution is determined by both the external and internal political situation. The author of the article also shows that the promotion of anti-Soviet and anti-Russian narratives in the Baltic states and the Ukraine is based on the lack of legal solutions. The verdicts of Soviet courts are used to portray collaborators as victims of the state system. Russia and Belarus as architects of the resolution continue to investigate Nazi crimes and give them the legal assessment.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.038 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".