Growth outlook for 2017 improves in Central Europe
Bibliographic record
Abstract
Subject The economic outlook after first-quarter data. Significance First-quarter GDP breakdowns indicate that Central Europe (CE) is experiencing a cyclical upswing after a subdued 2016, which saw economic growth slow to an average of 2.6% across the sub-region. Hungary and Poland have emerged as the two strongest economies, with each posting annualised GDP growth of 4.2% in the first three months, although Hungary’s quarterly growth rate was stronger than Poland’s. Impacts The supply side (particularly construction) and rising wages will support growth alongside an expected rise in disbursements of EU funds. Net exports will gradually make a stronger contribution to headline growth, in line with a pick-up in GDP across the euro-area and wider EU. Additional fiscal stimulus measures and accommodative monetary policy in Hungary and Poland are likely to lift GDP above forecasts. However, monetary policy may be tested given the prospect of an expected rise in inflationary pressures as wages continue to strengthen. Absent structural reform, labour shortages, population ageing and inconsistent labour productivity rates cast doubt over medium-term growth.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".