Bibliographic record
Abstract
Abstract This chapter analyzes whether there have been genocides or war crimes during the Russia-Ukraine war. The Ukrainian government and media, and initially some of the Western governments and the media, stated that the Russia-Ukraine war involved genocide of Ukrainians. In contrast, the Russian government and the media called the war presented the Russian invasion as a humanitarian intervention aimed at stopping a genocide of ethnic Russians and Russian speakers in Donbas. They denied Russian war crimes or claimed that they were staged. The analysis includes major cases of significant civilian casualties in Ukraine, in particular, in Mariupol, Bucha, Chernihiv, Vinnytsia, Donetsk, and various other locations in Ukraine and evidence-based estimates of civilian casualties. The chapter uses common definitions of genocide and war crimes in political science and the UN Genocide Convention and Geneva Conventions. It shows no evidence of a genocide but various evidence of war crimes, primarily, by the Russian forces.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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; both teacher heads agree on what is shown here.
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".