Bibliographic record
Abstract
In contrast to Russian studies, the study of crime and corruption in Ukraine is limited to a small number of scholarly studies while there is no analysis of the nexus between crime and new business and political elites with law enforcement (Kuzio, 2003a,b). This is the first analysis of how these links emerged in the 1990s with a focus on the Donbas (Donetsk and Luhansk oblasts) and the Crimea, two regions that experienced the greatest degree of violence during Ukraine’s transition to a market economy. Donetsk gave birth to the Party of Regions in 2001 which has become Ukraine’s only political machine winning first place plurality in three elections since 2006 and former Donetsk Governor and party leader Viktor Yanukovych was elected president in 2010 (Zimmer, 2005; Kudelia and Kuzio, 2014). Therefore, an analysis of the nexus that emerged in the 1990s in Donetsk provides the background to the political culture of the country’s political machine that, as events have shown since 2010 and during the Euro-Maydan, is also the party most willing in Ukraine to use violence to achieve its objectives.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".