Bibliographic record
Abstract
This article analyses why, after a quarter of century of post-Soviet transition, political parties in Ukraine remain weak. Ukraine's newly elected President Petro Poroshenko and his ally Kyiv City Mayor Vitaliy Klitschko both lead virtual political parties. The weakness of Ukrainian political parties is analysed through five impediments to their development: Soviet political culture; corruption and cynicism; provincial elites; regional and linguistic diversity; and weak party structure. The Soviet legacy has left an ideological wasteland in Eurasia upon which it has proven difficult to build political parties. The absence of pre-Soviet party roots from which to draw makes Eurasia different from the three Baltic States and Eastern Europe, while the late Soviet ‘era of stagnation’ and rapid, often violent and corrupt drive to a market economy in the 1990s deepened cynicism and corruption. The Soviet legacy of provincialism in non-Russian republics such as Ukraine remains predominant among business and political elites. Regional and linguistic diversity has negatively impacted on the ability of political parties to garner support throughout the country, undermining national integration, as seen during the Eastern Ukrainian violent counter-revolution in Donetsk, home base of the Party of Regions. Ukraine's parties remain structurally weak in their top-down approach and there is an absence of internal democracy, disrespect for voters and reliance on opaque sources of funding.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".