Can a leopard change its spots? Strategic behavior versus professional role conception during Ukraine's 2014 court chair elections
Bibliographic record
Abstract
Abstract Do judges respond to institutional and strategic incentives or do they strictly follow dominant professional role conceptions? This article weighs in by exploring whether an ideational shift toward judicial empowerment and independence can germinate from institutional reforms. Ukraine's 2014 Euromaidan revolution and the comprehensive judicial reform adopted in its wake provide a test of the competing theoretical accounts. A judicial lustration law sacked all incumbent court chairs, who had been appointed by the executive, and gave Ukrainian judges the right to elect new chairs via secret ballot. I analyze this radical step toward judicial self‐government using an original data set with individual‐ and court‐level data. The key finding is that less than a fifth of Ukrainian judges embraced their newly granted agency and elected a new chair for their court, whereas the overwhelming majority followed dominant professional norms of deference and reelected the sacked court chairs. This finding holds for all rungs of the judicial hierarchy and for all regions of Ukraine. Even protégés of ousted president Yanukovych won the secret ballot vote by their peers more often than they lost it. Beyond Ukraine, these results suggest that empowering individual judges in the highly hierarchical structure of a civil law judiciary is unlikely to lead to a judicial behavior shift, at least in the short run.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".