Assessment of the Governance Quality of the Departments of English in Saudi Universities: Implications for Sustainable Development
Bibliographic record
Abstract
Recently, numerous regulations and policies have been initiated in Saudi universities that support the Kingdom’s Vision 2030 of achieving smart, sustainable, and globally competitive universities. For the successful implementation of these regulations and policies, however, critical success factors including corporate governance have to be considered. Despite the extensive research on the importance of developing effective and reliable governance policies and practices for the overall growth of organizations including universities, no sufficient studies on the role of corporate governance in improving sustainable development plans and combating corruption, improving transparency, and enhancing sustainable development plans in the Saudi universities. This study, therefore, seeks to explore the impact of CG on improving accountability and sustainable development plans in Departments of English in Saudi universities. In-depth interviews were conducted with 48 participants, including the head of the English departments in four Saudi universities. Results indicate that the contributions of the universities to sustainable development plans and strategies are still under expectations. In this regard, the universities and higher education institutions in Saudi Arabia should replace the traditional academic model with the corporate model. The departments of English should address the changing needs of their candidates and students in this global world, and this has to be reflected in their sustainable development plans. Governance, however, should be enforced in all their operations as a critical success factor for sustainable development planning.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".