A Green Legacy: 30 Years of Manuscript Publishing Trends in the Electronic Green Journal
Bibliographic record
Abstract
This study examines the publishing and citation trends of the Electronic Green Journal: Professional Journal on International Environmental Information (EGJ) over the past three decades (1994–2024). This paper aims to provide a comprehensive analysis of research articles, top authors, countries, organizations, collaboration patterns, and highly cited articles. Bibliometric analysis was conducted using data extracted from the journal's metadata, Google Scholar database, and Google Scholar Profiles. A thorough search strategy was employed to ensure relevant data extraction. A total of 49 records (n=49) were selected for analysis using an Excel spreadsheet. The findings indicate 169 research articles were published during this period, with the highest number of articles published in the year 2000 and 2001 (n=20). The year 1994 garnered the most citations, totaling 1,767. Authors from the United States and Canada were the most prolific, contributing the highest number of research studies and author collaborations. Single authorship was the most common pattern, followed by collaborations between two authors. This paper provides an opportunity to examine the evolution of open international scholarly communication published in the EGJ over the past 30 years (1994–2024) and to highlight its most impactful contributions. Analyzing productivity and citation metrics, this study is the first to offer a detailed understanding of the environmental sustainability literature published in the EGJ.
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
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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.040 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".