Mobile Edge Computing: A Scientometrics Assessment of Global Publications Output during 2001–18
Bibliographic record
Abstract
The present study has examined mobile edge computing global research output, as indexed in Scopus database during 2001–18 on a series of Bibliometrics measures, such as publications growth rate, global publications share, citation impact, the share of international collaborative papers, distribution of publications by broad subjects. In addition, the study discusses the citation profile of top organizations and in fog computing, and the preferred media for research communication and characteristics of highly cited papers. The global research output (2716 papers) registered 42.48% annual growth rate and averaged to 6.77 citations impact per paper in the subject during the period. 80 countries participated in mobile edge computing research, of which the top 10 countries accounted for 89.51% global publication share and more than 100% of global citation share during 2001–18. China and USA tops the list of top 10 most productive countries in mobile edge computing research with 26.69% and 20.840% global publication share, followed by U.K. (7.29% share), Italy and Canada (6.44% and 6.11%), France (5.01%) and other 4 countries (from 3.94% to 4.86%) during 2001–18. USA tops the list (15.0 and 2.22), followed by Italy (10.73 and 1.59), Canada (9.59 and 1.42), Germany (8.89 and 1.31), U.K. (7.86 and 1.16), etc. in terms of citation impact per paper and relative citation index. 519 organizations and 561 authors participated in global mobile edge computing research, of which the top 15 organizations and authors contributed 21.80% and 8.21% global publication share and 22.54% and 9.71% global citations share during 2001–18. The world contributed 49.47% share of output in mobile edge computing research in top 15 most productive journals, and 28 of its papers have been rated as highly cited papers each with 101 to 844 citations per paper, averaging 222.61 citations per paper.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".