Model of Developing the Availability of Engineers to Innovative Activity for High-Tech Industry
Bibliographic record
Abstract
The article reveals the content of readiness for innovative activity as an integrative characteristic of the professional training of engineers for high-tech production.Various approaches to defining the concept of innovation are shown.According to various authors, the term "innovative activity" is considered as a type of activity, as a process, as a system of activities.The article presents the conceptual provisions used in the development of a model for the formation of engineers' readiness for innovation in high-tech production.Also shown is the author's interpretation of the structural components Models of the readiness of engineers to innovate for the hightech industry.The relationship between all components of the model is determined.The result of the developed model of an engineer for a high-tech industry is a specialist who combines both engineering training and knowledge in the field of management; capable of developing new high-tech products; conduct business planning in the innovation area; spend management audit of functional divisions of the high-tech industry; to develop strategic, tactical and operational plans, budgets of high-tech enterprises; to increase the efficiency of functioning of enterprises in knowledge-intensive industries, etc.This model, being universal, can serve as the basis for the development of methods, technologies for training specialists in other areas and can be translated for the organization of similar educational systems.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".