Bibliometric Analysis of Educational Research Articles Published in the Field of Social Study Education Based on Web of Science Database
Bibliographic record
Abstract
The aim of this study is to examine scientific articles related to Social Studies education in terms of bibliometric tools. Case study method, one of the qualitative research methods, was used in the study. The range of the relevant bibliometric data from the WoS database produced by Clarivate Analytics covers between 1975 and January 2020. In the study, bibliometric analysis technique was used. In the analyses made, it was revealed that 228 of the 64,338,472 studies registered in the WoS database were studies on Social Studies between the years 1975 and 2020. It was observed that 154 (67.54%) of these records were included in the education / training research category. In the analysis, it was seen that the most common type of publication related to Social Studies education was 154 studies and articles. However, it was determined that approximately 84 of the published articles have been published in the last five years. This rate shows that educational researches related to Social Studies education have increased in recent years. Again in the analyses, it was seen that there are 246 different authors contributing to the field. It was found that a total of 318 different keywords were used in 154 articles and the most effective journal was Theory and Research in Social Education magazine. In addition, it was figured out that researchers from 18 different countries published articles that contributed to the field. In this context, the most active countries are the United States, Turkey and Canada respectively. Turkey's ranking second amongst 24 articles from 18 different countries shows that it is influential in the field.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.185 | 0.186 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".