Bibliographic record
Abstract
This dissertation examines the dynamics of nonviolent resistance in repressive post-Soviet regimes through a comparative case study of Turkmenistan, Belarus, and Kyrgyzstan. It addresses the central question: What factors influence the onset and effectiveness of nonviolent resistance movements in these contexts? Drawing on 52 interviews and an extensive literature review, the study introduces a multilevel analytical framework encompassing macro-level factors (political and economic system, international relations and modernization), meso-level factors (power distribution in society), and micro-level factors (grievances, resources and organizational capacity of the resistance movement) to assess why nonviolent resistance may emerge and succeed in some contexts, but not in others. The comparison of the three post-Sovjet states reveals two overarching factors that are essential for nonviolent resistance: connectivity and strategy. In Turkmenistan, pervasive isolation of the country, the power, and the people precluded the emergence of any organized resistance movement. Belarus’s 2020 protests illustrate how widespread grievances and strong, newfound horizontal ties can trigger mass mobilization, yet lack of vertical ties and absence of a long-term strategy hinder success. Kyrgyzstan’s three revolutions demonstrate how robust vertical and horizontal connections can swiftly topple leaders, but can result in limited genuine change and "revolution fatigue" when a strong strategy for systematic reform is missing. The study concludes that nonviolent resistance thrives on multilevel connectivity and adaptive strategic planning. Without these, movements risk failure or short-lived victories, underscoring the complexity of resisting entrenched authoritarianism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".