A model for replacing human labor with industrial robots in the industrial production sector to promote sustainable growth
Bibliographic record
Abstract
The study aims to develop a model for replacing human labor with industrial robots in the industrial production sector to promote sustainable growth. A mixed-methods approach was employed, combining qualitative interviews with nine experts and quantitative analysis based on survey data from 500 industrial enterprises. Structural Equation Modeling (SEM) confirmed that government policies, industrial Readiness, integration cooperation, and production potential are key determinants of successful robot adoption. Government policy exerted the most substantial direct influence on industrial Readiness and integration cooperation, which, in turn, positively affected production potential. The results emphasize the importance of collaborative strategies between public institutions, educational organizations, and private enterprises to enhance workforce capability and technological preparedness. The proposed Automated Robot–Human Labor Industrial Replacement (ARHLIR) Model offers both theoretical and practical implications for policymakers and industry leaders. The integration of policy support, organizational Readiness, and cooperative networks ensures that automation drives competitiveness, innovation, and long-term sustainability.
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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 | 0.002 |
| 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 | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".