The Landscape of Digital Natives Research: A Bibliometric and Science Mapping Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.017 | 0.434 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it