A Bibliometric Study on Information Seeking Behavior Research in Library and Information Science using Biblioshiny and VOSViewer
Bibliographic record
Abstract
The primary aim of this attempt is to provide a thorough overview of Information Seeking Behavior (ISB) research within Library and Information Science (ISB) utilizing sophisticated bibliometric analysis tools. We searched the bibliographical data from Scopus and examined with the help of Biblioshiny and VOSviewer to demonstrate the visualization. A total of 1957 documents were included in the dataset. The growth rate is calculated at 7.88%. The total quantity of references quoted across all the documents in the dataset amounts to 57870. The study exposed that the highest number of articles (n=140) were produced in the year 2012 followed by the year 2013 (n=130). The study found USA was the top (Total production=1825) publishing country leading by a significant margin, followed by the UK, India, Canada, China, Iran, Australia, Nigeria, Pakistan, and South Africa. Library Philosophy and Practice leads with the highest number of publications, indicating its significant relevance and impact in the field, followed by other journals like Health Information and libraries journal, Journal of the medical library association. The study highlighted the frequency of usage of specific information and information-seeking terms in the dataset such as human and humans and information seeking as the most commonly used terms. The study delivers a bibliometric study of publications available in the Scopus database on ISB in the area of LIS of the period of 1966-2022.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.102 | 0.177 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".