Global Scientific Production on Neuroeducation: An Analysis in Scopus, 2010 – 2020
Bibliographic record
Abstract
The main characteristics of publications on neuroeducation by authors worldwide are described. A descriptive and retrospective analysis of articles 276 indexed in the Scopus database during the period 2010 to 2020 is performed, this database was chosen for having high quality scientific journals. The results indicate that the world scientific production is distributed in 10 documentary types, highlighting the publications of research articles (55.43%), the United States is the country with the highest production, followed by Canada, United Kingdom and Spain. The worldwide scientific production is visible in publications 139, Mind Brain and Education (United Kingdom) is the journal with the highest number of publications, followed by Frontiers In Psychology (Switzerland). The authors are mainly affiliated to European institutions, being Birkbeck, University of London (United Kingdom), the institution with the highest scientific production, also appears the Jaume I University, Iberoamerican institution with the highest production in this region. Finally, it was found that the most used keywords were the descriptors neuroeducation and neuropedagogy. We conclude that there is still a need for further research on neuroeducation and its implications for educational practice and policy based on knowledge of brain functioning. Even so, scientific production continues to be minimal compared to other psychoeducational variables, so it is necessary to increase international collaboration in research, based on institutions or groups of academics and professionals from different regions.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.104 | 0.183 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".