Unveiling Research Trends on the Sustainable Development Goals: A Systematic Bibliometric Review
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
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.
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.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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