Training for Industry 4.0: a systematic literature review and directions for future research
Bibliographic record
Abstract
Goal: This study aims to identify, synthesize and classify the main features explored by current research encompassing training for Industry 4.0 and propose directions for future research. Design / Methodology / Approach: The methodological procedure was oriented by a systematic literature review (PRISMA) methodology and followed by content analysis. After a review of academic databases, 78 papers dealing with training for Industry 4.0 were included and classified based on topics related to the science of training and Industry 4.0. Results: Most of the studies in training for Industry 4.0 are oriented to undergraduate and graduate students (in an educational approach) or industrial employees (in an enterprise approach) and, in general, they explore technical, technological, and human oriented subjects. There is a lack concerning studies targeting managers who deal with Industry 4.0 and few studies consider content related to Industry 4.0 impact on business models, sustainability, corporate social responsibility, and other related concepts. Limitations of the investigation: The main limitation is related to the database selection criteria. Search in non-indexed databases, book chapters, and non-English language are not included in this study. Practical Implications: The findings presented in this paper are relevant for researchers and academics as they can serve as a guide for future research work. Consultants, professionals, and trainers can enhance their courses by including currently less explored content or target audiences. Originality/Value: No similar papers were found in scientific databases and this reinforces this manuscript's originality and contribution.
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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 | 0.065 | 0.147 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".