Transnational Graduate Outcomes: A case study of the United Arab Emirates
Bibliographic record
Abstract
This study represents a modest first attempt to close this gap by examining the outcomes of UK TNE graduates in the United Arab Emirates (UAE). Through questionnaires and interviews with undergraduate and postgraduate UK TNE degree-holders, it identifies common experiences of graduates in their transition from education to employment and their application of specialist skills and knowledge to the spaces where they work and live. Some of the key findings are: - 84% of graduates expressed high or very high satisfaction with their teaching and learning experience. - 83% of respondents stated that their enthusiasm for further learning had increased as a result of their degree programme. - 84% of respondents felt that their programmes equipped them with both employment-specific knowledge and broad, transferrable skills. - 86% felt they were able to engage with diverse students and staff through their course and that they learned to collaborate in culturally diverse groups. - More than 80% reported that they made use of most of their skills, knowledge, and competencies in their jobs and over half felt they improved their career prospects with employers in the UAE and abroad. - 65% of respondents felt that they were doing well financially, although 67% believed that their career prospects had worsened to some degree as a result of the pandemic. - One quarter of respondents expressed the intention to work or study in the UK in the next five years, 39% were likely to develop professional links with UK organisations, and 67% were likely to visit the UK for holiday or leisure.
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 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".