Vocational Education in the Context of Modern Problems and Challenges
Bibliographic record
Abstract
The article analyzes the factors caused by the threat of spreading the coronavirus infection COVID-19 and introducing the martial law in Ukraine which affect the state of the vocational education. Taking into account the modern challenges and problems based on the analisys of the legislation the main directions of the vocational education development were determined. In particular, improving qualifications and professional development of teachers’ staff, enriching material and technical base of the vocational education institutions and educational programmes as well. Trendwatching of the modern labour market made it possible to single out its main trends: a change in the structure of employment, primarily an increase in the variability of employment; lifelong learning; automation and robotics; age diversity; forming hard skills, soft skills, digital skills; multipotentiality, background, interdisciplinarity. In order to solve the urgent problems and ensure the reorientation of the vocational training of qualified workers and improving its quality, special measures were suggested, including participating in the projects financed from the EU funds; developing educational modules and special courses for promoting lifelong professional development of teachers, improving educational programmes to enable improvement of the material and technical base of the vocational education institutions and professional development of teachers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".