With Regard to the Means and Priorities for the Development of the Professional Education System (The Experience of the EU Countries for Ukraine)
Bibliographic record
Abstract
The aim of the article is the analysis of means and priorities for the vocational education system development, and to comprehend the positive experience of the EU countries that can be implemented in Ukraine. For the realisation of the purpose the next methods were used: analysis, synthesis, comparison, abstraction, forecasting. In the results, it is noted that the Ukrainian structure of professional education differs from the European one in the absence of intermediary organisations that contribute to the educational process. The cooperation in establishing links between industries, firms and companies, and professional education institutions is at the level of private initiatives. It has also been found that the negative processes that hinder the development of the transformation of vocational education are uncompetitive teacher salaries and low levels of digital competence. Accordingly, this affects the low motivation to use innovative educational methods and technologies in education. The conclusions note the possibility of borrowing the French experience of the reorganisation of professional education with the formation of a structure in which students begin to receive professional education in the last grades of school.
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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 | 0.004 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".