Mobile computing: a scientometric assessment of global publications output
Bibliographic record
Abstract
The paper examines 34641 global publications output on mobile computing research, as covered in Scopus database during 2007-16. The study finds that mobile computing research is growing at 9.35% rate per annum and its citation impact averaged to 3.39 citations per paper. The global share of top 10 most productive countries ranged from 3.29% to 31.06%, with largest global publication share coming from China (31.06%), followed by USA (15.35%), etc. Together, the top 10 most productive countries accounted for 81.24% global publication share during 2007-16. Seven of top 10 countries achieved relative citation index above world average of 1: USA (2.37), U.K. (1.78), Italy (1.72), Canada (1.64), etc. International collaborative publications share of top 10 most productive countries in mobile computing research during 2007-16 varied from 11.55% to 48.16%. Computer Science, among subjects, accounted for the largest publication share (89.55%), followed by engineering (33.58%), social sciences (18.67%), mathematics (8.74%), etc. during 2007-16. The top 20 most productive organizations and authors contributed 14.79% and 1.76% global publication share respectively and accounted for 9.5% and 5.11% global citation share respectively during 2007-16. The top 20 journals accounted for 24.11% share of total journals output of 5673 papers during 2007-16. The top 50 highly cited publications registered citations in the range from 164 to 1235 citations per paper and together these top 50 papers cumulated 16822 citations, with an average of 336.4 citations per paper. These 50 highly cited papers resulted from participation of 184 authors and 103 organizations, and were published in 31 journals, including 4 in IEEE Transactions on Mobile Computing, 2 papers each in Decision Support System, IEEE Communication Magazine, IEEE Pervasive Computing and IEEE Communication Surveys & Tutorials and 1 paper each in other 26 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.018 |
| 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, 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".