Visualization of the Scholarly Output on Evidence Based Librarianship: A Social Network Analysis
Bibliographic record
Abstract
Abstract Objective – This paper aimed to analyze worldwide research on evidence based librarianship (EBL) using Social Network Analysis (SNA). Methods – This descriptive study has been conducted using scientometrics and a SNA approach. The researchers identified 523 publications on EBL, as indexed by Scopus and Web of Science with no date limitation. A range of software tools (Ravar PreMap, Netdraw, UCINet and VOSviewer) were utilized for data visualization and analysis. Results – Results of the study revealed that the United Kingdom (UK) and the United States (US) occupied the topmost positions regarding centrality measures, clearly indicating their important structural roles in EBL research. The network of EBL research in terms of the degree of connectedness showed low density in the co-authorship networks of both authors (0.013) and countries (0.214). Seven subject clusters were identified in the EBL research network, four of which related to health and medicine. The occurrence of the keywords related to these four subject clusters suggested that EBL research had a greater association with the setting of health and medicine than with traditional librarianship elements such as human resources or library collection management. Conclusion – This study provided a systematic understanding of topics, research, and researchers in EBL by visualizing the networks and may thus inform the development of future aspects of EBL research and education.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.028 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".