Measuring the Impact of Higher Education in Promoting Sustainable Development Goals: Analysis of the Arab universities’ performance
Bibliographic record
Abstract
Sustainability's global importance has amplified the role of universities in achieving the UN Sustainable Development Goals (SDGs) through research, education, and community engagement. Aim: This study aims to provide valuable insights into the role of Arab universities in promoting sustainability within the context of global higher education. Approach and methods: This study analyzes the performance of 200 universities from 15 Arab countries in the THE IMPACT Rankings. Their performance was compared to 135 universities from three of the leading world-class higher education systems: Canada, UK, and USA. Results: A global demand for sustainable development, Arab universities face challenges in improving their contributions to the SDGs and sustainability rankings. Despite the impressive growth in the participation of Arab universities in THE IMPACT Rankings yet lags in quality and impact of Arab universities contributions towards SDGs. Conclusion: The policy implications underscore the importance of institutional commitment to sustainability in Arab universities, urging the alignment of policies, funding, and curriculum with the United Nations Sustainable Development Goals (SDGs). Arab universities need to bridge performance gaps, promote green initiatives, and contribute effectively to the achievement of SDGs while recognizing the diverse socio-economic context and priorities of the Arab region.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 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".