Environmental Ethics In Socio-Humanities Research: A Hybrid Systematic Literature Review and Bibliometric Analysis
Bibliographic record
Abstract
This research combines two approaches, namely Systematic Literature Review (SLR) and bibliometric analysis, to see examine research on environmental ethics throughout the world. Data is taken from basic data Scopus, who initially discovered 17,684 publications from year to year 1969 to 2025. After the screening and eligibility checking process ,2,173 articles were selected for analysis (completed on 7 November 2025). Data visualization is done using VOSviewer The results show that the topic of environmental ethics is increasingly attracting the attention of researchers in various countries, especially in the United States, United Kingdom, and Canada which is the most productive country in this field. Some of the main themes that emerged in these studies include global justice, sustainability discourse, ecological citizenship, and philosophical foundations of environmental ethics. Based on these findings, this study proposes an integrated conceptual framework that connects cultural values, ethical principles, and social participation (as input) with ethical reflection and ecological engagement (as a process) to achieve comprehensive sustainability (as output). Overall, this study provides a comprehensive overview of global patterns, theme clusters, and conceptual directions in environmental ethics studies. Researchers also suggest that future studies expand data sources, for example by adding basic data Web of Science, and involve areas that have been underrepresented so that research results are more inclusive and culturally diverse. The broader implication of this research is the importance of integrating environmental ethics into sustainability policies, education, and social practices in society, in order to improve ethical awareness and ecological responsibility.
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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 | 0.074 | 0.136 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.246 | 0.204 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".