Overheating is among risks for Central-Eastern Europe
Bibliographic record
Abstract
Subject The economic outlook for the five leading CEE economies. Significance First-quarter GDP outturns in Central-Eastern Europe (CEE) were mixed. While Poland and Hungary surprised on the upside, the pace of economic expansion was less robust in the Czech Republic and Romania. After a strong cyclical upswing in GDP last year, economic growth appears to have peaked in the first quarter and economic growth is likely to moderate in 2018. Impacts In 2018, Romania, Slovakia and Poland will grow the fastest; the Czech Republic and Hungary will grow less robustly. With some economies managing to absorb EU funds at a faster pace than others, growth patterns are expected to diverge further. In Poland and Hungary, monetary conditions are expected to remain ultra-loose until at least end-2018. Weaker GDP growth rates are expected next year, particularly in the absence of structural reforms to tackle labour shortages.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".