Kin-State Intervention and the Securitization-Minority Policy Nexus: Hungarians and Russian-Speakers in Central and Eastern Europe
Bibliographic record
Abstract
Abstract Russia’s full-scale invasion of Ukraine in 2022, launched under the pretext of protecting the rights of Russian kin populations outside of Russia, had a massive impact on security concerns beyond Ukraine. An important consequence was the intensification of insecurities about the presence of large Russian-speaking minorities in Russia’s neighboring states. Scholars have long emphasized that kin-state involvement can lead to the securitization of minority issues, harming the willingness of governments to support collective claims by minorities associated with that kin-state. Yet there is scarce empirical knowledge about whether and under what conditions an assertive kin-state triggers securitization resulting in restrictive minority policy. We assessed this securitization-minority policy nexus comparatively, focusing on the impact of intensifying Hungarian and Russian kin-state activism on policies toward Hungarian and Russophone minorities in five states in Central and Eastern Europe. Our main finding is that intensified kin-state activism does not significantly disrupt previously established paths in minority policy-making, unless a kin-state turns to territorial revisionism. We also found that titular ontological insecurity (faced by actors belonging to a state’s dominant ethnic group) is a helpful heuristic for explaining instances when securitization results in policy restrictions, and we offer conceptual tools for analyzing the salience of both internal and external sources of titular insecurity. Ultimately, our findings highlight the necessity for scholars to distinguish between nonterritorial and territorial types of kin-state intervention in studies about the security dimension of kin-state involvement.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".