Catalyzing Regional Influence: The United Arab Emirates’ and Qatar’s Bids for Educational Leadership in the MENA Region through International Education Hubs
Bibliographic record
Abstract
Qatar and the United Arab Emirates are amplifying their focus on international education hubs to boost their regional leadership and project soft power in the MENA region.Sensing a propitious geopolitical climate, the two countries aim to enhance their regional influence through education.However, the capacity of education hubs to support the quest for regional leadership can be diminished by entrenchment in conventional organizational design and exposure to sustainability risks.he Gulf Cooperation Council (GCC) states-Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates (UAE)-are undergoing transformative change.Inseparable from this change is how these countries envision the role of internationalization in higher education.Nowhere is this role more evident than in the rhetoric regarding international education hubs in Qatar and the UAE.Drawing on policy documents and other forms of textual data from these two countries, our recent study finds a shift of focus from associating education hubs with capacity-building to increasingly associating these institutions with efforts to elevate the two countries' regional impact and ability to project soft power in overlapping geopolitical spheres of influence.Below, we briefly examine this shift of orientation, which could broaden our understanding of the nature of change it is orchestrating, the forces shaping the change, and the dynamic nature of cross-border education.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".