Management of Higher Education Institutions as a New Tool for the Development of Higher Education
Bibliographic record
Abstract
Higher education is extremely important for the socio-economic development and cultural enrichment of society, providing people with the relevant knowledge and skills to improve their skills and productivity in the context of further global development. Today, the task of effective resource provision and high-quality organization of the student learning process is extremely relevant in the world. The formation of a new mentality of all stakeholders in the educational process in a rapidly changing information environment is of great importance. This task requires constant monitoring and evaluation of the education system based on the collection, processing, and analysis of data necessary to make informed management decisions for the optimal development of higher education. The article aims to highlight the main patterns of management of higher education institutions with a view to their development reflected in the scientific literature, and to clarify certain practical characteristics of this process. In the process of preparing this study, the analytical and bibliographic methods, induction, deduction, and analysis were applied. The synthesis of information was used to study the scientific literature on issues related to the management of HEIs. Meanwhile, systemic-structural, comparative, logical, and linguistic methods, abstraction, and idealization were applied to study and process data. Among other things, the authors of the study conducted an online questionnaire survey to clarify certain aspects of this issue practically. Based on the results of the study, the theoretical aspects of the use of management tools as a tool for the development of HEIs have been studied. Moreover, some practical issues related to the management process in higher education have been characterized.
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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 | 0.015 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".