Bibliographic record
Abstract
The recovery of economic activity in Cyprus is forecasted to continue in the following quarters. Real GDP growth for 2015 is projected at 1.3%. Real output is estimated to expand (y-o-y) by 1.9 % and 2.6 % in the third and fourth quarter of 2015 respectively. The projected growth rates for the second half of 2015 should, of course, be interpreted in the light of the low levels of GDP reached during the corresponding period in 2014. Real GDP growth in 2016 is forecasted at 1.5%. The main drivers of the projected increase in real activity are given below. Growth (y-o-y) in real GDP and employment accelerated in the second quarter. Notably, the pickup in a number of activity-related domestic leading indicators continued during the third quarter. Stronger growth in the euro area and steady growth in the UK during the second quarter, as well as further increases in European economic sentiment indicators in the third quarter, support the recovery in Cyprus. The recent reductions in domestic lending interest rates amid conditions of weak demand and elevated unemployment are found to facilitate economic recovery. Furthermore, the return of domestic economic confidence to pre-crisis levels and the good fiscal performance are estimated to contribute to growth. Lower international oil prices and inflation in the EU are expected to benefit economic activity in Cyprus through their effects on real incomes, and on both domestic and external demand.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.452 | 0.290 |
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".