Green Chemistry in Education: A Bibliometric Study and Research Trends (2002–2022)
Bibliographic record
Abstract
Research on green chemistry in education is always evolving. Existing studies found that incorporating green principles into undergraduate education has significant benefits. In addition to the growing interest in green chemistry among researchers, a lot of literature evaluations on green chemistry have been conducted during the past ten years. This study presents the results of a bibliometric study of 444 articles on green chemistry in education from the Scopus database in the last two decades until 2022. The literature on green chemistry has previously been mentioned in an article published in 2021 about green and sustainable chemistry education (GSCE) in mainland China. This study was conducted to see research trends on green chemistry and its implementation in education for the last two decades (2002-2022) comprehensively. Based on the analysis’ findings using VOSviewer software, the most cited documents were written by Alfonsi, Tobiszewski, and Clark. The Journal of Chemical Education is the most prolific publishing source followed by the ACS Symposium Series and Physical Sciences Reviews. The nations having the most publications are the US, Canada, and the UK. Regarding the writers who contributed the most in this field are Dicks, Eilks, and Machado. The keywords most often used are “green chemistry”, “sustainability” “laboratory instruction” and “hands-on learning/manipulatives”.
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.
How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.151 | 0.361 |
| 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) | 0.003 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".