The Election of a Kleptocrat: Viktor Ianukovych and the Ukrainian Presidential Elections in 2010
Bibliographic record
Abstract
In 2010, Viktor Ianukovych, a candidate whose democratic credentials were disputed and whose shady background hardly inspired feelings of admiration or trust, was elected president of Ukraine. By asking the voters themselves on the eve of the election how such an individual could have won their votes, this article shows that when Ukrainians went to vote in 2010, they evaluated the qualities and the policy-issues associated with Ianukovych higher than those ascribed to his opponent, Iuliia Tymoshenko, even if only slightly so. In a Ukraine that since the Orange Revolution in 2004 has come increasingly to embrace democracy, the 2010 presidential elections marked a certain democracy fatigue that in the end came to favour Ianukovych’s “strong hand” image. Regional belonging is a usual factor in Ukrainian voting, and it played a role in the political assessments of the 2010 presidential election. However, issues of identity and language were among the lowest ranked in both eastern and western Ukraine, far behind the heated topics of jobs, unemployment, and welfare services. Later, identity-politics became more accentuated in the aftermath of the Revolution of Dignity in 2014 and the ensuing war between Ukraine and Russia. In 2010, what united many voters regardless of region was a stronger concern for jobs and welfare services than for democratic commitment in the candidates, or for identity politics. Those more personal issues paved the way for Ianukovych to become the president of Ukraine.
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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 | 0.003 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".