Features of the Application of Digital Technologies for Human Resources Management of an Engineering Enterprise
Bibliographic record
Abstract
The main purpose of our study is to form a demonstration model of the main processes for introducing digital technologies into the human resources management system for engineering enterprises. Digital transformations are associated with management changes, which are based on the technologies of the Internet of Things, artificial intelligence, blockchain, machine learning, Industry 4.0, Big Data in all spheres of public life. Investing in human capital has always been considered a productive investment. The digital economy has increased the urgency of increasing labor productivity through the transformation of human governance mechanisms. The main and key processes of the introduction of digital technologies in the human resources management system of the engineering enterprise were considered. The digitalization of society has radically changed people's lives and opened up new opportunities in the field of human resources management. The digital transformation of the human resources system affects all types of businesses, from large corporations to small micro-firms. As a result, the key stages and processes of implementation of digital technologies in the human resources management system of the enterprise were presented. The research methodology consisted of the application of modeling and graphical display methods.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".