A POLITICAL ECONOMY OF SECESSIONISM IN FEDERAL SYSTEMS
Bibliographic record
Abstract
ABSTRACT: The collapse of the USSR and Yugoslavia in the early 1990s taught the world that ethnic separatism can pose a grave threat even to the most powerful of countries. Canada’s flirtation with breakup in 1995 illustrates that this danger is not limited to new or postcommunist democracies. In each of these ethnic “hotspots, ” however, one also finds ethnic regions with decidedly weaker or even nonexistent separatist impulses: Uzbekistan in the USSR, Montenegro in Yugoslavia, and Nunavut in Canada, among others. This paper models the economic determinants of secessionism in a way that helps us understand such variation. Counterintuitively, it demonstrates that even in the absence of coercion, minority regions may opt to stay in a political union even when exploitation is the likeliest outcome and is considered a worse outcome than secession. This logic leads us to the important conclusion that the most eager seceders are likely to be those regions that are doing the best in the union rather than the worst. Empirical evidence is presented in the form of a quantitative analysis of ethnic minority regions in Russia, one of the world’s largest ethnofederations. The collapse of the USSR and Yugoslavia in the early 1990s taught the world that ethnic separatism can pose a grave threat even to the most powerful of countries. Canada’s flirtation
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 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".