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Record W4241175141 · doi:10.14429/djlit.37.11573

Metamaterials Research: A Scientometric Assessment of Global Publications Output during 2007-16

2017· article· en· W4241175141 on OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDESIDOC Journal of Library & Information Technology · 2017
Typearticle
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsnot available
Fundersnot available
KeywordsScopusCitation impactCitationChinaLibrary scienceMetamaterialWorld classWeb of scienceGeographyMathematicsPolitical sciencePhysicsComputer scienceEngineeringOpticsLaw

Abstract

fetched live from OpenAlex

<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>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 </span><span>published in 20 journals. </span></p></div></div></div>

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Not applicablelow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.029
metaresearch head score (Gemma)0.063
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics, Scholarly communication, Open science
Consensus categoriesMetaresearch, Bibliometrics, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0290.063
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.1460.149
Science and technology studies0.0010.001
Scholarly communication0.0070.019
Open science0.0080.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.565
GPT teacher head0.594
Teacher spread0.030 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it