Bibliographic record
Abstract
Education in Europe has a long historical tradition as a tool for social structuring and cultural progress. Over the centuries, the skills cultivated in schools have continuously adapted to the prevailing cultural, economic, and technological needs. From the ancient societies of Greece and Rome to the Renaissance and the Industrial Revolution, the skills integrated into education have reflected the demands of each era. However, modern Europe faces new challenges, such as digital transformation, climate change, and social inequalities, which shape the strategic direction of the European Union’s (EU) education policies. The EU recognizes the importance of education as a driver of development, not only in terms of professional adaptation but also in fostering active citizens capable of participating in society and advancing democratic processes. Education policies focus on strengthening fundamental skills, such as communication and critical thinking, while also emphasizing new, contemporary skills, such as digital and environmental competencies, to meet the demands of the 21st century. The Digital Compass 2030 initiative serves as a key tool in preparing citizens for emerging technological challenges, while the Sustainable Development Strategy promotes environmental awareness and sustainability. This study aims to examine the evolution of knowledge and skills in European education, emphasizing how modern educational strategies integrate technological and social challenges. Specifically, it highlights the significance of school years as a foundation for developing skills that foster collaboration, critical thinking, and social responsibility. This study adopts a multidimensional approach that encompasses social, economic, and environmental dimensions, identifying the role of skills in preparing students for the challenges of the future.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".