[no title]
Bibliographic record
Abstract
The study proposes a smart decision-making tool for skills needed to select the ideal technology profile for the workforce 4.0.Based on the design science methodology, we first performed a systematic literature review using the PRISMA methodology to identify skills underlined by the literature.A relative importance index (RII) was then adopted to rank the skills.Our findings show that soft skills are essentially similar regardless of digital technology.Additionally, hard skills seem more diversified owing to the technology presented.Furthermore, our findings suggest that each technology requires a different level and range of skills.A dashboard was built to present a smart decision-making tool for mapping skills related to Industry 4.0.This research contributes to the needs of the workforce by identifying and recruiting profile definitions, implementing role-changing and change management, and reskilling and upskilling people in companies that embarked on the digital initiatives of Industry 4.0, defining a skill-based technology profile 4.0.The paper presents contributions on a tool to assist leaders and Human Resource Management 4.0 to effectively manage skills in times of digital transformation.Thus, the paper is original in that it presents a decision-making tool to help relate and integrate a worker's skills in Industry 4.0 and its disruptive technologies, establishing a decision of the ideal technological profile 4.0 of the ideal worker for Industry 4.0.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.006 |
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".