Bibliometric Analysis of Studies on Environmental Education in Early Childhood Education
Bibliographic record
Abstract
The aim of this study is to conduct a bibliometric analysis of articles related to environmental education in early childhood (EEEC). The sub-objectives of the study were to determine the distribution of studies on EEEC by years, the most used keywords, the most cited articles, the most active researchers, the most cited journals and the most active collaborating countries. Bibliometric analysis methods were used in the research. Bibliometric mapping analysis was preferred to provide visual representations of the relationships between the main concepts. The data were obtained from the Web of Science (WoS) database and the 191 articles accessed because of filtering were analysed using VOSviewer software. As a result of the research, it was determined that studies on EEEC have increased over the years. Among the keywords, “early childhood education”, “environmental education” and “sustainability” came to the fore respectively. The most cited study was “Beyond Stewardship: Common World Pedagogies for the Anthropocene”. Prominent journals included “Australian Journal of Environmental Education” and “Sustainability”, while the most influential authors were identified as “Alsina, A.” and “Rodrigues-Silva, J.”. Among the countries, “Australia”, “Canada” and “Brazil” are at the forefront. Encouraging more co-operation and interdisciplinary studies in the field of EEEC will help deepen research in this 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 | 0.013 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.196 | 0.238 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".