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Record W4390689910 · doi:10.35784/preko.5384

Measuring the Impact of Higher Education in Promoting Sustainable Development Goals: Analysis of the Arab universities’ performance

2024· article· en· W4390689910 on OpenAlexaboutno aff
Saleh A. K. Saleh, Heba M. Adly

Bibliographic record

VenueProblemy Ekorozwoju · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSustainability in Higher Education
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityContext (archaeology)Sustainable developmentCurriculumPolitical scienceHigher educationEconomic growthEducation for sustainable developmentQuality (philosophy)EconomicsGeography

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.331
Teacher spread0.310 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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