The Reinstated Gubernatorial Elections in Russia: A Return to Open Politics?
Bibliographic record
Abstract
Rescission of the direct popular election of Russia’s regional executives was interpreted by many observers as a cardinal indicator of the trend toward authoritarianism in Russia. In the wake of a series of vocal national protests against electoral fraud following the State Duma elections of December 2011, President Dmitrii Medvedev announced the reinstatement of gubernatorial elections, and a federal law was passed in May 2012 implementing this decision; the first gubernatorial elections under the new system took place in five Russian oblasts on 14 October 2012. This paper analyzes the campaigns and outcomes of these elections, as well as media and expert commentary surrounding them, with a goal of exploring whether the reinstitution of a gubernatorial electoral process presents a significant potential for renewed political competition or popular political mobilization at the regional level in Russia. Analysis of these early cases suggests that while these elections offer a real potential for genuine electoral contestation, the political establishment also exhibited the ability to apply a variety of formal and informal mechanisms to assure the desired outcome. Realization of the potential of gubernatorial elections to promote electoral competition would require a greater resolve and unity of opposition parties, including at the subregional level.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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 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".