Bibliographic record
Abstract
This article examines coercive capacity and its impact on autocratic regime stability in the context of post-Soviet Armenia, Belarus, Georgia, and Ukraine. In the post-Cold War era, different types of coercive acts require different types of state power. First, high intensity and risky measures – such as firing on large crowds or stealing elections – necessitate high degrees of cohesion or compliance within the state apparatus. Second, effective low intensity measures – including the surveillance and infiltration of opposition, and various forms of less visible police harassment – require extensive state scope or a well-trained state apparatus that penetrates large parts of society. Coercive state capacity, rooted in cohesion and scope, has often been more important than opposition strength in determining whether autocrats fall or remain in power. Thus, the regime in Armenia that was backed by a highly cohesive state with extensive scope was able to maintain power in the face of highly mobilized opposition challenges. By contrast, regimes in Georgia where the state lacked cohesion and scope fell in the face of even weakly mobilized opposition. Relatively high scope but only moderate cohesion in Belarus and Ukraine has made autocratic regimes in these countries generally more effective at low intensity coercion to prevent the emergence of opposition than at high intensity coercion necessary to face down serious opposition challenges.
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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 | 0.002 | 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 | 0.007 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".