Unveiling Research Trends on the Sustainable Development Goals: A Systematic Bibliometric Review
Bibliographic record
Abstract
Sustainable Development Goals advocated by the United Nations in 2015 focus upon five major crucial areas of concern by 2030 i.e., people, planet, prosperity, peace, and partnership.Through a bibliometric analysis, the present study intends to examine the trends, development, and prospects of the Sustainable Development Goals from 2016 to 2023.The study employed VOSviewer, MS Excel, and Biblioshiny (R Studio) to examine data collected from the Web of Science core collection database.In total, 2,814 title-based articles were analyzed and refined.Various methods were employed to identify the multidimensional contribution to the research of SDGs, including analysis of keywords, prolific authors, productive journals, active institutions and countries, and collaborations.The study identified significant clusters of SDG themes, such as environmental sustainability, education and attitude towards sustainability, and improvement in health quality and women's participation.The study also identified the top publications, prominent authors and journals, active institutions, research gaps, and nations contributing to this domain.The results show that high-income nations have a notably higher level of deliberation regarding SDG research.The results revealed significant implications, offering insightful information to stakeholders, researchers, and policymakers to prioritize future research endeavors and resource allocation to best achieve the 2030 SDGs.
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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 | 0.060 | 0.193 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.245 | 0.210 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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, 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".