Russia and the Far-Right: Insights From Ten European Countries
Bibliographic record
Abstract
Russia’s influence over far-right/ racially or ethnically motivated violent extremist (REMVE) milieus in Europe is multi-faceted and complex. It involves direct activities, such as financing or political support, as well as indirect activities, such as disinformation campaigns. In some cases, Russia was associated, albeit remotely, with some far-right violent incidents in Europe, including the alleged coup attempt by the sovereign movement Reichsburger, in Germany. Recognising the increasingly confrontational policy of Russia vis-à-vis Europe, and the growing threat from far-right extremism in Europe, this book thoroughly and systematically reviews Russia’s relationship with diverse far-right actors in ten European countries over the past decade. The countries covered in this book include Austria, The Czech Republic, France, Germany, Hungary, Italy, Poland, Serbia, Slovakia, and Sweden. The chapters are authored by some of the world’s most authoritative experts on extremism and Russian influence. Overall, this edited volume is the first such comprehensive attempt at mapping the scope and depth of Russian influence over far-right extremism in Europe, resulting in the identification of key patterns of influence and offering some possible recommendations to counter it. This book is both a leading scholarly work, as well as a wake-up call and guide for action for European policy-makers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".