“We've Got to Kill Them”: Responses to Bucha on Russian Social Media Groups
Bibliographic record
Abstract
On the weekend of 1–3 April 2022, Western media sources broke news of a number of civilian corpses uncovered in Bucha, a satellite town to the northwest of Kyiv from which invading Russian forces had recently retreated. Images of bodies lying in the streets of the town, in cellars and buildings, and with hands tied and marks of rape and other torture rapidly went viral on Twitter and other social media networks. Social media users and politicians began to label what had occurred in Bucha an act of genocide. On Monday 4 April, Ukrainian president Volodymyr Zelensky visited the town and accused Russian forces of “genocide” and “war crimes.” 1 Western governments have largely concurred; Canada’s Parliament, for instance, voted unanimously to label Russian behaviour in Ukraine a “genocide.” 2 Scholars have long debated Russia’s history of genocide – or genocidal behaviour – in its war in Chechnya. More recently, they have explored the country’s role in aiding the Syrian regime in committing its own genocide. 3 However, potentially genocidal behaviour under the Putin regime has never been so widely discussed by the western public as in the case of Bucha.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".