Bibliometric analysis : Green chemistry trends and issues in chemistry education from 2019 to 2024
Bibliographic record
Abstract
This study analyzes green chemistry research trends in chemistry education. This study used a quantitative bibliometric approach. The number of publications analyzed is 104 publication documents from 2019 – 2024. This research collects, processes, and filters information in Scopus journals and articles. Metadata results show that the distribution of publication frequency peaked in 2019, with 26 documents identified. The green chemistry research area is dominated by chemistry research (31.3%). The country with the most documents and the most productive in publishing green chemistry is the United States, with 30 papers identified. At the same time, Indonesia is ranked fifth as the most productive country in publishing green chemistry, with 10 documents identified. Canada ranked second with 17 papers, and Germany ranked third with 15 documents. The institutions that contributed the most came from Germany: the University of Bremen, with 11 papers 10.58%, and the University of Toronto with 8 documents (7.69%). The authors with the most citations are Chen Tse-Lun et al., with 245 citations. Meanwhile, when viewed from the number of documents published by the author, Eilks I. has 11 papers with a contribution of 4.91%. There are 5 clusters with the most popping keywords: green chemistry, human, and chemical reaction. Research and publications on this topic have been sparse in the past five years. Surveys and analyses of green chemistry literature are essential because tracking research trends in green chemistry in chemistry education is vital to directing 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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.078 | 0.150 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".