Mobile Cloud Computing: A Scientometric Assessment of Global Publications Output during 2007-16
Bibliographic record
Abstract
The paper examines 3779 global publications on mobile cloud computing research, as covered in Scopus database during 2007-16, experiencing an annual average growth rate of 139.6% and qualitative impact averaged to 4.22 citations per paper. The top 10 most productive countries individually contributed global share from 2.91% to 22.41%, with largest global publication share coming from China (22.41%), followed by USA (19.32%), etc. Together, the 10 most productive countries accounted for 85.74% share of global publication output during 2007-16. Five out of 10 countries have scored relative citation index above the world average of 1: Malaysia (2.41), USA (1.87), U.K. (1.79), Canada (1.43) and Italy (1.16) during 2007-16. The international collaborative publications share of top 10 most productive countries varied from 9.74% to 67.19% in mobile cloud computing research during 2007-16. Computer Science, among subjects, contributed the largest publication share (85.79%), followed by engineering (28.37%), mathematics and social sciences (10.64% and 5.58%) etc. during 2007-16. The top 20 most productive organizations and authors together contributed 18.92% and 9.98% respectively as their share of global publication output and 37.07% and 24.28% respectively as their share of global citation output during 2007-16. Among the total journal output of 5673 papers, the top 20 journals contributed 30.01% share to the global journal output during 2007-16. Of the total mobile computing research, the top 15 highly cited publications registered citations from 99 to 848 and they together received 3834 citations, with 255.60 citations per paper. These 15 highly cited papers involved the participation of 26 authors and 27 organizations. These 15 highly cited papers were published in 8 journals, of which 4 papers were published in IEEE Communication Surveys and Tutorials, 2 papers in Mobile Networks and Applications, and 1 paper each in other journals.
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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 | 0.021 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.123 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".