Bibliographic record
Abstract
More or less all Central and East European transition countries (CEECs) experienced an economic downturn in 1998, which bottomed out in the winter of 1998-99. Year-on-year GDP data point to decelerated growth in the fourth quarter 1998 and in the first quarter 1999 in a smaller subgroup (Hungary, Poland, Slovakia and Slovenia) and GDP decline in others (the Baltic states, Bulgaria, Croatia, the Czech Republic, Romania, Russia). In nearly all CEECs the situation improved in the course of 1999. In Russia, the currency crisis of August 1998 led to substantial depreciation, which in 1999 stimulated domestic production. Regional trading partners of Russia (the Baltic states, Ukraine and to a lesser degree also Bulgaria and Poland) started to recover from the breakdown of Russian demand for their exports. The Czech Republic, Hungary, Poland and Slovenia profited from an improving business climate in Germany, their most important trading partner. Home-made policies supported the upswing – especially a decline in interest rates in most of the countries, which also helped to keep real currency appreciation moderate. Inflation increased somewhat in the CEECs with lower inflation rates when world market prices for oil started to rise. Inflation in the CEECs is not expected to come down to Western European levels in the immediate future, a fact which will make it difficult to find an adequate interest rate policy. The degree of economic inequality between CEECs is remarkable. Thus, differences in the per capita GDP are greater in these countries than in the EU. In addition, price levels, recalculated in USD terms, are more divergent between them than between western countries. The year 2000 could become the first year in which CEE economies record no negative GDP growth rates. Even Russia has at last entered a path towards growth. Next year, growth could even strengthen and the more successful countries (currently Hungary, Poland and Slovenia) may be able to lower their income gap relative to the EU.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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; both teacher heads agree on what is shown here.
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".