APPLICATION OF FOREIGN EXPERIENCE IN EDUCATION MANAGEMENT IN TERRITORIAL COMMUNITIES
Bibliographic record
Abstract
The article examines the management models of general secondary education at the local level in Finland, Canada and Singapore. The importance of analyzing the educational systems of local communities in the context of tendencies towards decentralization of management in various spheres of social life is emphasized. Since education and training are key to the socio-economic progress of the country, it is useful for Ukraine to study and use foreign experience in education management, which covers a variety of approaches, strategies and methods that can contribute to the improvement of its own education management systems. Finnish education policy was found to be based on the principles of decentralization and democracy, giving local governments and school boards considerable autonomy. In Canada, education management is mixed in nature, with centralized and decentralized elements, the provinces are responsible for education, but receive funding from the federal budget. In Singapore, the education management system is centralized, significant attention is paid to the continuous professional development of teachers and the use of innovative approaches to learning and teaching. It was concluded that the analysis of the experience of education management at the level of local communities in different countries of the world indicates the importance of Ukraine's search for its own, unique approach based on the best practices of the global community. However, before adapting this experience, it is necessary to take into account the peculiarities of our mentality, history and cultural attitudes. Active exchange of experience with countries such as Finland, Singapore and Canada can be carried out through partnerships between territorial communities, educational institutions, international organizations and government agencies. Adapting the experience of these countries to Ukrainian realities and the needs of specific communities is extremely important. When implementing foreign experiences, it is necessary to take into account the opinions of various stakeholders, including teachers, parents, students, administrators and community organizations. This will contribute not only to the improvement of the quality of education at the level of a separate territorial community, but also to the achievement of success in the field of education at the level of the entire state.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".