Traditions and Innovations in Career Guidance Work of Railway Universities
Bibliographic record
Abstract
Systematic career guidance counselling work organized by a higher education institution is a prerequisite for attracting highly motivated applicants who have consciously made their professional choice. The need to increase the visibility of educational institutions among Russian applicants, form mechanisms for talent selection, introduce digital tools for promoting educational products, and improve the quality of targeted training, actualize the need to bring career guidance to a new development level, while maintaining the best traditions and using the potential of innovative solutions. The aim of the study was to assess the current state of career guidance work of higher education institutions of railway profile with schoolchildren and applicants and to identify possible ways to improve it. The theoretical basis of the study consists of the works of domestic and foreign specialists, revealing the essence and content of career guidance work of universities. The methodological basis is the system-activity approach. General scientific methods (analysis, synthesis, comparison, generalization, classification) are used as theoretical research methods, traditional and content analysis of documents is used to collect empirical information. The study made it possible to identify traditional and innovative forms and methods of career guidance, as well as the main factors that determine the nature and specifics of career guidance programs implemented by universities of the railway profile. The authors come to the conclusion that the career guidance work carried out at the universities of Railway Transport the main provisions of the Concept of training personnel for the transport complex until 2035. Thus, along with the traditional, innovative formats of career guidance activities are implemented, but reserves for the use of «new media», networking, artificial intelligence technologies, virtual and augmented reality, and gamification in the process of career guidance.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".