Scientific production on environmental education: a bibliometric analysis
Bibliographic record
Abstract
The study presents a bibliometric analysis of scientific production on environmental education, using the VOSviewer software to map collaboration between authors and countries, as well as the co-occurrence of keywords. A total of 3,319 works published between 1996 and 2020 were analyzed, focusing on interdisciplinary and multidisciplinary collaboration. The analysis revealed a low level of collaboration among researchers, despite the significant increase in publications over the last decade. Most of the high-impact authors are based at universities in the United States, such as Stanford and Cornell. Regarding international collaboration, the United States leads, followed by Australia, the United Kingdom, Canada, and Spain, all economically developed countries. The keyword analysis indicated that terms such as "environmental education," "sustainability," and "sustainable development" are strongly interconnected and present in the majority of studies. The study concludes that environmental education has become an increasingly relevant field of research, with growing academic interest and potential to influence public policies. However, there is a need for greater collaboration between researchers from different areas and countries to enrich the field and address existing gaps. The bibliometric methodology used in the study provides an overview of the evolution of research in environmental education, identifying the main trends and challenges for the future.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.171 | 0.245 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".