Electronic Resources Management (ERM): A Scientometric Study of Global Publications during 1999-2018
Bibliographic record
Abstract
T he present study analyses the global research productivity on “electronic resources” during 1999-18 as covered in Scopus, a multidisciplinary international database. Research productivity is examined using both qualitative and quantitative indicators like year wise research output, growth rate, citation impact, share of international collaborative publications, prolific authors, leading institutions, broad subject areas, medium of communication and high cited papers etc. The data is retrieved using keywords such as “electronic AND library*” OR “e-resource” OR “e-journal” OR “e-book” OR “electronic book” OR “electronic journal” OR “electronic theses and dissertations”. The ERM publications registered 7.84% annual growth and its citation impact averaged to 5.03 citations per paper during 1999-18. The top 10 most productive countries together contributed 84.64% global publication share and 85.1% global citation share of the total global publications during 1999-18. Social Sciences, among subjects, accounted for the highest publication share (88.37%), followed by computer science (26.24%), arts and humanities (6.7%), medicine (5.77%) business, management &accounting(4.37%), etc. during medicine (3.86%).The top 10 most productive organisations and authors contributed 10.84% and 5.03% publication share and 12.04% and 2.5% citation share respectively during1999-18. Among the 50 highly cited publications (with citation per paper ranging from 31 to 168 citations), the largest number (24) of publications came from U.S., followed by 9 from UK, 3 from Australia, 2 each from Malaysia, Canada, India and 1 each from Brazil, Ghana, Cameroon, Gambia, Nigeria, Slovakia, Taiwan, Tanzania, Trinidad and United Arab Emirates etc. These 50 highly cited publications involved 104 authors and 60 organisations and were published in 32 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.038 | 0.082 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".