Trends in the “Europe 2020” Strategy An Overview
Bibliographic record
Abstract
Eight indicators corresponding to the targets of “Europe 2020” Strategy were used to estimate the deviation of EU Member-States from their targets. The study showed that: i) the distance to the employment target of 75 % of people aged 20-64 years has narrowed, ii) the expenditure for R&D as a percentage of GDP are still below the target of 3%, iii) the reduction of greenhouse gas emissions in ESD sectors by 20 % compared to 1990 levels are still below the target, iv) the increase of the share of renewable energy in final consumption to 20 % remains just below the target, v) the move towards a 20% increase in energy efficiency shows a good prospect, vi) the reduction of school drop-out rates to less than 10 % is steadily approaching its target, vii) the share of population aged 30-34 having completed tertiary education to at least 40 % is steadily approaching its target, viii) the lifting at least 20 million people out of risk of poverty was not achieved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".