Virtual Hatred: How Russia Tried to Start a Race War in the United States
Bibliographic record
Abstract
During the 2016 U.S. presidential election, the Russian government engaged in a sophisticated strategy to influence the U.S. political system and manipulate American democracy. While most news reports have focused on the cyber-attacks aimed at Democratic Party leaders and possible contacts between Russian officials and the Trump presidential campaign, a more pernicious intervention took place. Throughout the campaign, Russian operatives created hundreds of fake personas on social media platforms and then posted thousands of advertisements and messages that sought to promote racial divisions in the United States. This was a coordinated propaganda effort. Some Facebook and Twitter posts denounced the Black Lives Matter movement and others condemned White nationalist groups. Some called for violence. To be clear, these were posts by fake personas created by Russian operatives. But their effects were real. The purpose of this strategy was to manipulate public opinion on racial issues and disrupt the political process. This Article examines Russia’s actions and considers whether they violate the international prohibitions against racial discrimination and hate speech.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".