A Comparison Of GDP Growth Of European Countries During 2008-2012 From The Regional And Other Perspectives
Bibliographic record
Abstract
The aim of the article is to compare the total real GDP growth of European countries from the 3rd quarter of 2008 with the 3rd quarter of 2012, the period characterized by a predominant economic stagnation or economic recession in the majority of examined European countries. The countries are divided into groups based on the following grounds: whether they are geographically close to the economic center (Germany) or peripheral, whether they are in the eurozone or not, whether they are (new) EU members or ‘old’ ones, etc. The main findings from the comparisons are as follows: 1. European countries close to the economic center (Germany and its neighbours) experienced, on average, positive economic growth during examined period, while countries from European periphery on average experienced negative economic growth during the same period. This difference was found statistically significant at the α = 0.01 level. 2. Differences between eurozone and non-eurozone, old and new EU members, and between more and less populated countries were found statistically insignificant. 3. European regions with the most negative real total GDP growth included the Baltics, the Balkans, Southern Europe (Italy, Portugal) and Iceland. The most successful countries with the most positive real total GDP growth were central European countries (Poland, Slovakia, Germany, Switzerland, Austria) and those in northern Europe (Sweden and Norway).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".