Understanding 21st century skills needed in response to industry 4.0: Exploring scholarly insights using bibliometric analysis
Bibliographic record
Abstract
• Bibliometric analysis of 2662 articles by 6579 authors on 21st century skills. • Research on 21st century skills has grown exponentially. • The field is dominated by psychology, education and technology researchers. • Industrial engineering and nursing are two prominent fields of research. International policy agendas are increasingly focusing on the 21st century skills needed by future workers in response to Industry 4.0. In this study, we conduct a bibliometric analysis of 2662 articles published by 6579 authors in the last two decades to understand the structure of the scholarly knowledge in this field. We first identify influential articles, documents, journals and trends in this literature. We use co-citation analysis to identify foundational themes in the development of 21st century skills literature, then using bibliometric coupling, we identify communities in the current research front. We then use co-word analysis to identify future directions in the field. Overall, we find that research on 21st century skills has grown exponentially in the past two decades, however, few researchers focus primarily on this topic. The existing research is primarily dominated by psychologists, education researchers and technology researchers. We also find that specific disciplines such as industrial engineering and nursing are prominent contributors in the field, and that critical thinking and computational thinking are key areas of focus.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.048 | 0.077 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".