ЗАРУБІЖНИЙ ДОСВІД ФОРМУВАННЯ ЦИФРОВОЇ КОМПЕТЕНТНОСТІ МАЙБУТНІХ МЕНЕДЖЕРІВ
Bibliographic record
Abstract
The article systematizes different approaches to the interpretation of the concept of «digital competence of future managers», depending on the context and scope of application: competence approach, pedagogical, information and communication, integration, system, analytical and ethical approaches. The main focus of the article is on the features of the formation of digital competence of future managers in different countries. A comparative analysis of the approaches used in Great Britain, Germany, the USA, Finland, Sweden and Canada was carried out. Domestic universities are emphasizing the use of digital platforms for online courses and practical assignments. Germany focuses on digital management, innovation and the integration of modern technologies into business practices. MBA programs in the USA include studying Data Science, IT management and digital strategies for leaders. Finland offers courses in digital platforms, data analytics and innovation management. Sweden focuses on digital marketing and real business case studies. In Canada, educational programs focus on digital governance, blockchain technologies and data analysis. In addition, the article determines that the application of foreign experience in the formation of digital competence in Ukraine will contribute to positive dynamics in this issue. In particular, domestic universities will be able to borrow the use of digital platforms for learning by integrating platforms for online courses, such as FutureLearn, or by creating their own national counterparts. It has also been determined that the German approach to integrating IoT, AI and Big Data into curricula can be adapted through the creation of disciplines based on current business technologies. At the same time, the Canadian experience can be useful for creating training programs in blockchain technologies and data analysis, which will allow future managers to work with modern financial and management tools. Accordingly, the Swedish approach to using real cases (for example, from Spotify or Ericsson) can be implemented through the partnership of Ukrainian universities with leading local and international companies.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.027 | 0.011 |
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".