Authoritarian Dissent Management: Repression of the Nonsystemic Political Opposition in the Post-Soviet Region
Bibliographic record
Abstract
This dissertation aims to advance scholarly understanding of autocratic repression by analyzing patterns of state-led violence against political opposition candidates in the context of presidential elections. I challenge the conventional wisdom of the ‘Law of Coercive Responsiveness’, which posits that bigger threats lead to more repression (Davenport 2007, 7). I demonstrate that a political opposition candidate with robust international prominence poses a large political threat due to their potential ties to other powerful actors—whether foreign leaders, international organizations, or transnational activist networks—who can express sympathy, raise awareness for that actor and their cause, enact sanctions, and/or leverage others to act. However, I argue that greater threat does not always correspond with more repression, and large threats can be safeguarded by a political opposition candidate’s robust international prominence. It is not only that robust prominence abroad is an adequate indicator of political threat for these critical opposition candidates, but also that prominence abroad corresponds with a decrease in the likelihood that a political opposition candidate will encounter state repression. This is because hybrid leaders are concerned ex-ante for what will happen ex-post (Lachapelle 2017, 11), which makes them less likely to repress political opposition candidates with assumed foreign networks that may intervene in anticipation of, or in response to, human rights violations. I evidence this claim using an original dataset of 4,083 potential presidential candidates across the post-Soviet region from 1991-2018 and a mixed-methods approach that includes both quantitative modelling and in-depth case studies.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".