An Analysis of the Performance of European Union Countries in the Light of Europe 2020 Strategy Indicators
Bibliographic record
Abstract
In this paper, an analysis of the Europe 2020 strategy indicators has been carried out. The strategy defined three priorities for European countries: Smart, Inclusive and Sustainable growth. These goals (developing an economy based on knowledge and innovation, fostering high employment levels, and promoting a more resource-efficient and greener economy) were measured by eight headline indicators, related to employment, research and innovation, renewables and energy, education and poverty. For each indicator, a target has been set, and the eight indicators are subject to regular statistical monitoring and reporting. Europe 2020 is perhaps not a complete set of indicators for measuring the progress of societies and the quality of life of their citizens, but it is a very important recognition of European institutions that GDP alone is not enough and that it must necessarily be integrated with measures that take into account equity and sustainability. The paper analyses the trends of the Europe 2020 indicators, considering the target reached or not, synthesizing the results using an Alkire-Foster method and clustering the 27 European countries, in order to highlight convergence processes among the Member States (MSs) in the ten years taken into account by the Strategy. After almost10 years, the EU has not reached most of the targets set in 2010, and many MSs are well behind schedule.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.026 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".