TEN TOP PROBLEMS OF EDUCATION. FROM COGNITIVE DISSONANCE TO THE ALGORITHM OF THE FUTURE RENAISSANCE
Bibliographic record
Abstract
The article analyzes the radical transformations of classical education, characterizes the peculiarities of foreign and national system of education reaction to the challenges of a modern innovative society. It has been outlined that in many countries of the European Union, North America (the USA, Canada), the East (Japan, China), new schemes for the division of higher education programs into professional and academic ones are being developed and implemented, a system of narrow-profile higher educational institutions is being formed, research and corporate universities come into being. At all levels of higher education the aims, theory and practice of training prospective specialists are reconsidered. In addition, it has been shown that in Ukraine, modern problems of reforming education, on the contrary, lack system and consistency, in programs and slogans of subjects of educational policy there are often elements of populism, and setting of unrealistic tasks. As a result, many participants of the educational process have a sense of cognitive dissonance both when trying to assess the true state of the academic environment and evaluate the models that are offered. Based on comprehension of the most important points of bifurcation in modern Ukrainian education, ten key problems are identified and characterized. It is proved that solving them and ensuring the renaissance of education is possible, at least, based on three viable steps: introduction of a new organizational and economic mechanism for innovative development of education; reconstruction of the content and methodological resources of education; audit of the academic environment and optimization of the network of higher educational institutions.
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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.068 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".