Scientific production on environmental education: a bibliometric analysis
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.011 | 0.054 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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