A bibliometric analysis and science mapping of scientific publications of Alzahra University during 1986–2019
Bibliographic record
Abstract
Purpose Currently, the evaluation of scientific performance of universities is one of the important indicators in various ranking systems. One way to evaluate the academic performance of universities is to analyze the scientific documents of universities in reputable international databases. The purpose of this article is to analyze and evaluate the scientific publications of Alzahra University (Iran) as the top 100–200 universities during 1986–2019. Design/methodology/approach This study was performed using bibliometrics and visualization techniques. The Scopus database was used to collect data. Affiliation search and advanced search were used to retrieve the data. Excel, VOSviewer and CRExplorer software were used to analyze the data. Findings The results showed that the scientific publications and received citations by Alzahra University documents during the time have been upward. At the national level, it was the most scientific collaboration with researchers at the University of Tehran. Also at the international level, the most scientific collaboration has been with the United States, Canada and Germany. In total, 80% of scientific publications were published by 20% of authors. Also, 70% of the highly cited articles were published in journals with quartile 1. Finally, clustering results showed that Alzahra University's scientific publications are in five main categories, including “chemistry,” “physics,” “biology,” “psychology and educational sciences” and “accounting sciences, management, and computer science.” Originality/value This study could be a good model for evaluating the performance of scientific productions of universities and scientific institutions with bibliometrics and visualization approaches.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.049 | 0.055 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".