THE GLOBAL RESEARCH TRENDS ON THE EARLY LITERACY IN EARLY CHILDHOOD EDUCATION
Bibliographic record
Abstract
This study examines global trends in early literacy articles published in the Web of Science (WoS) database. Using a descriptive survey model and bibliometric analysis, 2879 articles from 1985 to 2023 were analyzed. According to the results, the number of articles and citations has increased significantly since 2005. The Journal of Early Childhood Literacy is the most prolific journal in this field. Most studies originate from developed countries, notably the USA, Canada, and Australia, with English being the predominant language. Key contributing institutions include the State University System of Florida and Florida State University. The author Justice, L.M., is a leading contributor with 42 articles. The most cited article, by Sénéchal & LeFevre, at 2002 has 1044 citations. The 'Education & Educational Research' category leads with 1751 articles, covering topics like education, psychology, and language and linguistics. Literacy, emergent literacy, phonological awareness, reading are the prominent keywords and recent trends include topics such as literacy environments and dual language learners. Researchers from the USA, Canada, and Australia are prominent in collaborative efforts. This study analyzes global trends and key contributors in early literacy research, providing valuable insights for researchers and decision-makers about future directions and research gaps 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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.046 | 0.090 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".