A Bibliometric and Thematic Analysis of Educational Neuroscience Research in Early Childhood Education, 1970–2024
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
This review employed bibliometric methods to test the meta-data of documents related to educational neuroscience in early childhood education (ECE) published over a period of 55 years, from its beginnings in 1970 to 2024. The study analysed a total of 498 documents. Using bibliometric techniques, it summarised descriptive trends, uncovered the foundational intellectual framework, identified popular themes, and suggested new avenues for future research. Thematic analysis highlighted the evolution of themes across three distinct developmental phases. The integration of bibliometric techniques with thematic analysis offered a comprehensive overview and deeper understanding of the historical, present, and future trajectories of educational neuroscience research in ECE. Research Findings: There has been a notable increase in educational neuroscience publications in ECE, with a significant surge since 2021. The United States, Canada, and China are the leading contributors. Influential research primarily examines the impact of brain injury or neuropsychological deficiencies and the efficacy of intervention programs. The intellectual structure consists of three main research clusters, while conceptual themes focus on neurodevelopment, interventions, and neuro damage. Additionally, eight prominent research fronts were identified. Practice or Policy: The findings have implications for future educational neuroscience research in ECE, methodology, policy, and practice.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.039 | 0.080 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it