Continuous Vocational Education of Employees in Conditions of Knowledge Economy: European Trends and Prospects of Ukraine
Bibliographic record
Abstract
One of the current trends in the world is formation of knowledge-based economies. A significant role in this process plays continuous vocational education and training of employees of enterprises as the main economic actors. The results of correlation analysis presented in the paper confirm the importance of on-the-job training and its interconnections with development of knowledge-based economy, competitiveness of the country and its economic growth. Considering Association Agreement between Ukraine and the European Union it is advisable to pay special attention to the peculiarities of vocational education and training in EU Member States. The complex study of the relationship between lifelong learning, on-the-job training, employment, development of the knowledge economy, economic growth and competitiveness within each country allows to divide the countries into two groups. This division was implemented according to correlation ties between the selected indicators. Taking into account the limited amount of funds spent on vocational education and training of employees at Ukrainian enterprises it is suggested to focus on creating organisational conditions for stimulating the development of self-education and professional self-improvement of employees. The establishment of the systems of organisational knowledge at enterprises is considered as the foundation for the development of such conditions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".