Bibliographic record
Abstract
The education processes within the knowledge society of the twenty-first century involve a complex analysis and transformation of learning and teaching proposals. The ubiquitous penetration of technology and especially of ICT contributes to new cultural profiles of social, political and economic organizations and of course also has an impact on education. The decentralization, personalization, increased flexibility, technologic convergence, and other effects of telematic networks call for both an extension of educational programs in the framework of lifelong learning programs and also require measures to overcome exclusion in the face of the new social, technologic and economic demands. Strategic learning, the teaching of comprehension, virtual collaborative groups, and teachers as facilitators – both in face to face and remote education – will help to develop autonomy, strengthen communication and technological abilities, and foster problem solving skills in order to make decisions and participate in the improvement of quality of life through flexible structures, open mentalities, and equitable ethical values. Within this framework, learning and teaching in the knowledge society of the twenty-first century will be conceived for personal self regulation and social self sustainable alternative development. The scenarios include creative competencies and flexible attitudes through the practice of comprehensive and critical reading and thinking, emotional education, free expression, contrasted transference into reality, and participation within diversity. The latter implies a respect to local identity to foster the search for universal peace, democratic coexistence and continuous improvement.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".