Čvert' stolittja ekonomičnych reform v Ukraïni: zapizno, zamalo, zanadto povìl'no?
Bibliographic record
Abstract
CASE-Center for Social and Economic Research prepared an Ukrainian translation of Oleh Havrylyshyn's publication "A quarter century of economic reforms in Ukraine: too late, too slow, too little". The first aim of this paper is to describe the main developments in the Ukrainian economy since its independence in 1991, focusing on the evolution of output, and the path of economic reforms - that is, to simply show what happened. The bottom line on that is well known: Ukraine's economy performed very poorly, and its reforms moved quite slowly, lagging behind most of Central Europe and the Baltic, and even behind some FSU (Former Soviet Union) countries. This first task is a relatively easy one, though some measurement issues do need discussion. In comparison, the second aim - explaining why it happened, identifying the explanatory, causal factors - is much more difficult and contentious. Indeed, causation here means two dynamics: the relationship between performance and reform pace, and the underlying determinants of the slow reforms. The paper's main effort will be to argue and present evidence that the poor economic performance is primarily due to the late and slow start on economic reforms. However, it only begins to point to the explanations for slow reforms and suggest a modeling approach to analyze this econometrically in future work. Contents of this publication were first presented by Oleh Havrylyshyn during the 135th mBank-CASE Seminar "A quarter century of economic reforms in Ukraine: too late, too slow, too little".
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".