Where English, neoliberalism, desire and internationalization are alive and kicking: higher education in Saudi Arabia today
Bibliographic record
Abstract
The internationalization of higher education globally continues to grow more and more towards commercialization and neoliberalism paths, despite growing concerns about the underlying consequences. Building further on our work and using Saudi Arabia as a national case, this article critically investigates how and in what ways the Saudi government's desire to internationalize its higher education system has overlooked the many problems associated with its English-only policy, and the neoliberal shaping of social and economic pressures. The article also demonstrates the paradoxical messages concerning internationalization success, strategies, and visions conveyed by the Saudi government and by several institutions from English-speaking countries in response to Saudi Arabia's aspiration for internationalization of its higher education. We draw on several data sources in this article, specifically: (1) the Colleges of Excellence (CoE) project documents – a major Saudi government's initiative to restructure the technical and vocational education system; (2) Several publicly available news items released by technical and vocational colleges from Canada and the UK as well as by the UK government in relation to their participation in Saudi Arabia's CoE project; and (3) publicly available news items published in a number of key local Saudi newspapers regarding various aspects of the CoE project.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".