Bridging or Dividing? Chinese Language Integration and the Resilience of ELT in Saudi Higher Education under Vision 2030: Policy Symbolism vs. Pedagogical Realities
Bibliographic record
Abstract
Saudi Arabia has pledged to attract international investments as part of its Vision 2030 policy, one that seeks to diversify the education and economic environment of the country, especially by enhancing alliances with China. In support of this objective, some higher education institutions have established or initiated the teaching of the Chinese language within English-dominant educational environments, in addition to the currently taught English Language courses. This paper examines the implementation of Chinese in ELT practices in Saudi Arabia, focusing on the deployment of institutional methodologies, stakeholder viewpoints, and the ideological consequences of the policy-based multilingual curriculum revolution. The study employs a multiple-case design that is based on language policy and planning (Ricento, 2006) and translingual pedagogies (Garcia & Wei, 2014). It relies on semi-structured interviews and responses to these interviews by key stakeholders of the case, as well as analysis of institutional documents and a faculty, administrative, and student survey. Insights indicate an ongoing antagonism between English as a linguistic capital of dominance (Bourdieu, 1991) and the novel valorisation of Chinese as a diplomatic language of the state. The research offers critical perspectives on how Saudi higher education navigates the linguistic pluralism of Vision 2030's framework and suggests implications for multilingual curriculum design, trainer preparation, and language policy implementation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".