A Green Legacy: 30 Years of Manuscript Publishing Trends in the Electronic Green Journal
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
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.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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