Metamaterials Research: A Scientometric Assessment of Global Publications Output during 2007-16
Bibliographic record
Abstract
The paper examines 9858 global publications output on metamaterials research, as covered in Scopus database during 2007-16. The study reveals that metamaterials research registered 15.27% growth and averaged citation impact to 10.08 citations per paper. The global share of top 10 most productive countries in metamaterials research is 84.97 % and their individual global share ranged from 3.30% to 25.57%. China accounted for the largest global share (25.71%), followed by USA (23.96%), U.K. (6.06%), India (5.26%), etc. Five of top 10 countries scored relative citation index above the world average i.e. more than 1: Germany (2.06), USA (1.81), U.K. (1.49), Canada (1.03) and Spain (1.01). The international collaborative publications share of top 10 most productive countries varied from 6.14% to 59.80%. Physics and astronomy, among subjects, contributed the largest publication share (59.36%), followed by engineering (56.71%), materials science (33.30%), computer science (20.32%), mathematics (6.74%) and chemistry (4.46%). The top 20 most productive organisations and authors together contributed 24.69% and 13.17% global publications share respectively and 35.72% and 25.96% global citation share respectively. The top 20 journals accounted for 45.97% share of global output (5743 papers) reported in journals. Of the total global output on metamaterials research, 52 papers were found as highly cited papers averaging 535.64 citations per paper in 10 years. These 52 highly cited papers involved the participation of 310 authors and 142 organisations and were published in 20 journals.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.086 | 0.200 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".