Transformation of Education Processes and Preparation of Competencies for the Digital Economy
Bibliographic record
Abstract
In this article, the problem of training specialists with digital competencies for the agricultural sector, as the main industry, necessary for the food security of the state. The analysis of the views of researchers on the issues of teaching youth in the context of global digitalization is presented. The analysis and generalization of information about modern technologies in the system of training personnel for the agro-industrial complex, taking into account the experience of the Omsk State Agrarian University, with the support of modern information and communication technologies. The idea is substantiated that digitalization of production and management processes in the agro-industrial complex is impossible without hard and soft skills with new competencies. The article summarizes new material based on the results of a survey of rural youth in Russia and Kazakhstan on the problem of professional self-determination. The characteristic features of modern students and their self-positioning in the conditions of a changing professional environment are highlighted and described. Special attention in the work of the authors is focused on the need to form an educational trajectory, which is based on the symbiosis of classical agricultural education, practice-oriented learning, project activities, concepts - technologies, e-learning and other digital educational resources. The conclusion reveals the authors' opinion on the forecast trends on the issue under study.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".