“Genocide of the Soviet People”: Putin’s Russia Waging Lawfare by Means of History, 2018–2023
Bibliographic record
Abstract
This article exposes the political underpinnings of the term “genocide of the Soviet people,” introduced and actively promoted in Russia since 2019. By reclassifying mass crimes committed by the Nazis and their accomplices against the civilian population—specifically Slavic—as genocide, Russian courts effectively engage in adjudication of the history of the Second World War. In the process, genocide trials, ongoing in twenty-five Russian provinces and five occupied Ukrainian territories, present no new evidence or issue new indictments, thus fulfilling none of the objectives of a standard criminal investigation. The wording of the verdicts, and a comprehensive political project put in place to promote it, suggests three main objectives behind the novel genocide of the Soviet people trope, absolving the Soviet Union of responsibility for the outbreak of the Second World War, counterbalancing the efforts of the Ukrainian government to seek international recognition of Holodomor as an act of genocide, and drawing a parallel between Nazi crimes and those ascribed to “Ukrainian neo-Nazis.” Russian genocide trials are a crass example of sham justice and a manifestation of lawfare.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".