A Bibliometric Analysis: A Tutorial for the Bibliometrix Package in R Using IRT Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The bibliometrix package in R programming language, which is frequently used in bibliometric analysis, was introduced in this research. The article aimed to illustrate the various analyses applied in a bibliometric study. For this purpose, articles containing the "item response theory" (IRT) or "item response modeling" or "item response model" terms in the abstract were searched in the Thomson Reuters Clarivate Analytics Web of Science (WoS at http://www.webofknowledge.com), and bibliometric data was downloaded. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) steps were followed in the study. Data from 3388 IRT-related articles on education and psychology, searched between 2001 and 2021, were used in the study. Data were analyzed with the bibliometrix package. Some of the stages in data analysis were shared with screenshots. As a result of data analysis through the real data set, the author’s keywords related to IRT were item response model, differential item functioning, psychometrics, assessment, measurement, reliability, validity, Rasch model, and measurement invariance. The countries with the highest number of citations in IRT studies were the USA, Canada, Netherlands, United Kingdom, and China, respectively. Turkey ranked 12th in IRT studies with 434 citations. It was thought that bibliometric analysis of articles related to IRT would shed light on researchers in the field of psychometrics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.255 | 0.624 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it