THE CONCEPT OF SMART EDUCATION AS A FACTOR IN ENHANCING DIGITALIZATION AND INTELLECTUALIZATION
Bibliographic record
Abstract
The relevance of the study of smart education as a factor in enhancing digitalization and intellectualization is that HEIs are struggling to keep up with the changes in technology in higher education. For the first time, the idea of wisdom formation was proposed and developed by philosophers. Philosophically, the starting point and aim of wisdom formation is the awakening and development of human 'wisdom' . True education should help people to know themselves, eliminate fear and awaken wisdom 1 . The famous British philosopher Whitehead put forward the theory of child wisdom education, believing that the theme of learning is life and the purpose of learning is to discover the wisdom of students. Subsequently, Smart Education attracted the attention of educators, psychologists, and scientists at home and abroad. Max van Manen, the founder of Canadian phenomenological pedagogy 2 put forward the concept of wisdom pedagogy focused on children's development, pointing out that educators should create a caring school environment for children and pay attention 1 Buhaychuk Oksana, Nikitenko Vitalina, Voronkova Valentyna, Andriukaitiene Regina, Malysh Myroslava. Iteraction of the digital person and society in the context of the philosophy of politics.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".