Model Minorities and Fifth Columns in Service of Nation-Building: (De)securitization of Ethnicity in Nation-States with Multiple Minorities
Bibliographic record
Abstract
Abstract Does the presence of two or more transborder minorities alter the logic of nation-building and affect minority securitization? This article goes beyond the triadic nexus framework commonly applied to minorities caught between their home- and kin-states, proposing a complex lens for analyzing states with multiple ethnic minorities. Titular political elites dealing with multiple minorities assign them to contradictory frames to manage the challenging reality of ethnic demography and regional security. By framing one minority as a “model minority” — trustworthy and law-abiding — and another as a “fifth column” — threatening and disruptive – they accomplish two aims: (1) maintain the dominant status of the titular nation by discrediting minority claims for institutional changes, and (2) legitimize the differential treatment of minorities. Ethnic minorities’ responses to these frames vary from relative acquiescence to violent conflict. I explore why the initially excluded Poles have been recently accommodated in Lithuania, why the marginalized Uzbeks became targets of repression in the Kyrgyz Republic, and why the relatively accommodated Russian speakers, former colonizers, became framed as a security threat in Lithuania but not in the Kyrgyz Republic after the Russian invasion of Ukraine. Understanding how strategic framing advances nation-building offers generalizable insights on (de)securitization of ethnicity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".