Continuous Vocational Education of Employees in Conditions of Knowledge Economy: European Trends and Prospects of Ukraine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it