A bibliometric analysis of the factors influencing global research conditions of teacher education
Bibliographic record
Abstract
This bibliometric analysis explores global research trends in teacher education, examining 1,757 publications from the Lens.org database (1878-2023). The study reveals a significant upwelling in teacher education program research, particularly since 2015, with journal articles dominating the publication landscape. Arthur Tatnall emerged as the most prolific author, while psychology, medicine, and medical education were identified as the top contributing fields. The analysis highlights the United States, United Kingdom, Australia, China, and Canada as leading research nations, underscoring a concentration of output in developed countries. Key influencing factors identified include the impact of the COVID-19 pandemic, technological integration, and the need for adaptive and equitable strategies. The findings emphasize the interdisciplinary nature of teacher education program research and the critical need for increased capacity building and international collaboration, especially in underrepresented regions.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.122 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".