Bibliometric mapping of top papers in Library and Information Science based on the Essential Science Indicators Database
Bibliographic record
Abstract
This study analyzed top papers published in the field of Library and Information Science (LIS) published between 2009 and 2019 and included in the Web of Science (WoS) subject category “Information Science & Library Science”. Data of the 501 top papers were extracted from the Essential Science Indicators (ESI) database comprising 499 highly cited papers and 16 hot papers in the field. The distributions of document type, language of publication, scientific output, and publication of journals are reported in this paper. The co-authorship network visualization of authors, organizations and countries, co-occurrence network visualization of all keywords are visualized using VOSviewer software. The 501 papers, all written in English language, were from 1,579 authors employed at 680 organizations based in 59 countries/territories. The papers were published in 40 journals in the field. The top 5 core journals ranked based on the impact factor (IF) were MIS Quarterly, Journal of the American Medical Informatics Association, International Journal of Information Management, Journal of the Association for Information Science and Technology, and Information Management. The top 5 organizations were University of Maryland (USA), University of Wolverhampton (UK), Vanderbilt University (USA), Indiana University (USA), and Wuhan University (China). Authors from the following countries contributed the most - USA, People’s Republic of China, England, Canada and Netherlands. Based on network map using VOSviewer, there were micro, meso and macro level collaborations based on common interests in a specific topics. Analysis of all keywords showed that the research were distributed into 6 clusters. This study concludes that one important characteristic of top papers is the journal reputation, therefore authors can choose their ideal journal with a high JIF and quartile to publish papers in the English language related to this research field.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.029 | 0.095 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.055 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".