The Dual Education System as a Key Element for Future Railway Experts at the Beginning of the 21st Century
Bibliographic record
Abstract
The article deals with the dual education system as a key element for railway experts training at the beginning of the 21-stcentury. The author points out that the dual education system is of key importance for railway experts training as it provides balanced growth and in-depth knowledge of the major, develops the hard and soft skill of future specialists. It is stated that a similar system of education existed in Ukraine in the second half of the 21-stcentury. This system was rather successful and guaranteed new professional employees for the railway industry. The author demonstrates some information on the number of hours that were given for dual education in the past. The paper also draws out attention to the current situation in Ukraine, shows the main trends and the progress of the dual system of education nowadays. The author concludes that the most effective way of teaching future railway experts is to teach them in the enterprise giving more than 50% of total credits for practical,not theoretical courses. Thus, it is important to use historical experience and to implement it in the present education system.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".