The Post-Communist Far Right and Its Transnational Linkages
Bibliographic record
Abstract
Abstract In this special issue, our contributors move the academic conversation beyond methodological nationalism and approaches that analyze far-right movements only within their respective state contexts by interrogating the circulation of ideologies, funds, and people across sociopolitical boundaries. Our goal is to scrutinize the far right in post-communist Eastern Europe by examining the multitudinous and multidirectional ties that exist between groups at the local, regional, national, and transnational levels. Attention, moreover, is paid not just to those factors that facilitate such linkages, but also to the obstacles that hamper these flows via various detours, omissions, and other forms of resistance. In this introduction, we offer a theoretical overview and discussion of contributors’ findings to argue that conduits for the dissemination of far-right discursive frames are hardly unidirectional in nature. As a result, the transitological narratives of progress and regress typically invoked to explain the emergence of the far right offer only a partial understanding of how it mobilizes, builds alliances, and circulates ideas. We unpack the conceptual pitfalls and fallacies of transitological narratives and instead foreground the concept of multidirectionality, which opens up new avenues through which to understand how far-right groups mobilize and disseminate their narratives.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".