The Landscape of Digital Natives Research: A Bibliometric and Science Mapping Analysis
Bibliographic record
Abstract
This article provides a panoramic view of the research undertaken so far in the field of digital natives to highlight important trends, examine the intellectual structure and provide recommendations for future research. The study uses 983 publications from the Scopus database to analyse the productivity, impact and research performance of nations, journals, authors and institutions using indicators such as impact factor, h-index and citation counts. This review study uses bibliometric and science mapping analysis to assess the most recent developments and trends in ‘digital native’ research. VOSviewer is used to conduct keyword network analysis, co-authorship analysis and reference co-citation analysis, followed by SciMAT analysis to provide an evolution map and a cluster of themes. The analysis identified key contributors to the field, high-impact papers and geographic areas where field research is concentrated. The main research gaps were then identified, indicating future research avenues. The findings of this study will provide fresh higher-level insights into the developing field of digital native research for educators, computing executives, business managers and research scholars. Such information would be useful in establishing digital native recruitment tactics by the respective industry. The research will also aid in the development of policies for digital natives.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.065 | 0.097 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".