Bibliographic record
Abstract
The effects of the COVID-19 pandemic were global and led to an economic decline in most countries of the EU. The development and values of economic indicators varied from country to country and showed significant regional differences. The study evaluates the coverage of selected economic indicators in the Member States of the EU in the period 2010–2020. The analytical part is based on empirical statistical data. As a methodological procedure for testing the convergence of the EU, we compared the results of the coefficient of variation of GDP per capita in PPP and the unemployment rate. The findings of this study confirm the predicted development trends. The pandemic has reversed major convergence trends. Divergence within the EU was affected by a lower decline in GDP in the developed countries of the EU. The tendencies of social disparities in the unemployment rate were different from the development of the coefficient of variation of GDP per capita. The first year of the pandemic marked a decline in disparities between the countries of the EU. For future research, we recommend monitoring the development of convergence in the next pandemic period.
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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 | 0.001 | 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 | 0.000 | 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".