Bibliographic record
Abstract
The main purpose of the considerations is to present and analyze the most important morphological features of cyclical fluctuations for the 27 European Union countries as a whole (aggregated indicator -EU27) and for individual EU countries in the period from the first quarter of 2000 to the fourth quarter of 2022 based on the gross domestic product dynamics indicator.On the basis of the constructed indicators, which made it possible to isolate business cycle fluctuations, the degree of synchronization of the cycles of individual EU regions with the EU27 reference cycle was assessed.Design/methodology/approach: The methodological foundations of the research process and the empirical assessment of regional business cycles in the EU were preceded by theoretical analyzes regarding the concept, essence and morphological features of regional business cycle fluctuations.The study is based on 92 observations for each studied region.Dynamic indicators were built reflecting changes in general economic activity, i.e. quarterly time series of GDP.The obtained series were decomposed and deseasonalized.Upper and lower turning points were identified, resulting in phases of growth and slump in a given economy.This made it possible to present full business cycles and then assess them (in particular, determine the degree of synchronization between individual countries and the reference cycle).Findings: By assessing the course of fluctuations in business cycles of the entire economy of the European Union as a whole and fluctuations in business cycles of individual regions making up the EU in the period from the first quarter of 2000 to the fourth quarter of 2022, it can be concluded that this progression is not uniform.Divergences in business cycles in the European Union are an important feature of the data.This differentiation depends largely on the specific development of each region.Originality/value: The course of cyclical fluctuations was determined for all countries that are members of the European Union, as well as in individual EU regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".