A CASE FOR THE SOCIAL SCIENCES AND SOCIAL CAPITAL IN THE HIGHER EDUCATIONAL SYSTEM AND ECONOMY OF UKRAINE
Bibliographic record
Abstract
The article is devoted to the study of social sciences and social capital in the system of higher education and economy of Ukraine. The article aims to solve two tasks: The first - is to discuss the, not simple, relationship between the natural and social sciences in stimulating technological innovations; The second - is to show how the new concepts of social and cultural capital are connected to economic development. The main results of the article are presented in a study of scientific works devoted to the relationship between the social and natural sciences and how this relationship relates to educational and economic development. The methodological basis of the research is the methods of comparative analysis of scientific research in the field of social and natural sciences, the authors of which investigated human, social and cultural capital. The article also examines the main scientific discussions on the role of social and cultural capital. These are relatively new topics that are increasingly recognized as important components of development. It is stated that the humanities and social sciences should occupy a prominent place in education because, paradoxically, these subjects stimulate technological innovation and economic growth in modern knowledge economies. This view coincides with the school of New Institutional Economics (New Institutional Economics) and the school of "human relations" (human relations) in the field of management, which emphasize social and cultural factors for the effective functioning of organizations and economic development. The technocratic or scientific management paradigm has reached the limits of its usefulness in education, innovation, and economic progress. This paradigm now needs to be supplemented by more open educational systems and organizations, whose functioning is enhanced by cultural and social capital.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".