A bibliometric study of world research output on e-resources during 2006–2016
Bibliographic record
Abstract
A bibliometric study is generally used to measure the literature output on any given subject. Bibliometric analysis use data on numbers and authors of scientific publications and on articles and the citations therein (and in patents) to measure the "output" of individuals/research teams, institutions, and countries, to identify national and international networks, and to map the development of new (multidisciplinary) fields. Similar approach has been adopted in this paper to identify the global literature output on e-resources. The research data used for the study has been retrieved from ‘Scopus ’database source. The study in hand attempts to identify the bibliometric characteristics of the research publications from Scopus database during the study period 2006–2016 (11 years). A total of 137051 publications have been identified. Bibliometric techniques have been used to analyse the data. The collected data was classified by using Excel Spreadsheet. The results show that the total publication output (137051) shows a stable trend in citation on yearly basis, the articles (56.8%) are the most prominent publications, and the most prolific author is Bates, D.W with 159 articles and Denny, J.C. with 96 publications. The most productive institution is V.A. Medical Centre which produced 1349 publications, followed by University of Toronto which produced 1006 publications. ‘Journal of The American Chemical Society ’is highly productive journal with 2362 (1.72%) publications. United States of America tops the list of countries with publication output being 43121 (31.46%) publications. The majority of the e-resource publications are produced by the subject of Medicine with 43.3% publication output.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.060 | 0.136 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".