Educational Research in Türkiye: A Bibliometric Analysis of the Global Literature
Bibliographic record
Abstract
This study aims to conduct a bibliometric analysis of Türkiye's present role and contributions to international education research literature. For this purpose, a total of 8630 publications covering the years 1992-2021 in four categories in the Web of Science Core Collection (WoS) covering to educational research were analyzed. The investigation used a variety of metrics to examine the number of publications, publication impacts, publication citation histories, the most influential publications, publishers, institutions, and the international collaboration structure. According to the study's findings, almost all of the publications were released after 2006, and there was a significant increase in the number of publications until 2012. Despite this growth, however, the impact of the publications declined. Productivity and publication impact values have further decreased since 2013. The highest number of publications was in the journal Education and Science. The majority of international collaboration has been with the USA, but the most impactful publications have been with Canada. Middle East Technical University has the greatest impact on publications, while Hacettepe University is the most productive institution. The six main areas of educational research that Türkiye has concentrated on are common issues in education, learning and teaching, psychology, science education, teacher training, and scale development.
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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 | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.125 | 0.167 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| 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, 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".