Structural-Functional Model for Corporate Training of Specialists in Carrying Out Mentoring
Bibliographic record
Abstract
Embeddedness of mentoring in the professional activity demands from company specialists not only a high level of their psycho-pedagogical formation, but also others which include common cultural and professional competencies for effective corporate training of interns and young employees. The purpose of the article is to develop a structural-functional model of corporate training of technical specialists in mentoring in the conditions of modern production. The leading method is modeling, allowing consideration of this issue as task-oriented and organized process for improving the professional, common cultural competences, and for formation of special competences of company specialists, that they will need to effectively carry out the mentoring activities. The structural-functional model of corporate training of technical specialists in carrying out mentoring in modern production includes objective, methodological, content-related, organizational and procedural and efficiency components. The model aims at integrating professional production and psycho-pedagogical training of teachers, in which the improvement of their professional and interprofessional competencies for conscious and responsible management of their changes in professional development, as well as for the solution of psycho-educational and organizational-methodological problems of interns’ corporate training.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".