Language Policy and Power in Out-of-Classroom EFL Contexts: The Case of English Departments in Saudi Arabian Higher Education Institutions
Bibliographic record
Abstract
Most of the research on language policies in educational institutions has hitherto focused on the creation, interpretation, or appropriation of language policies that govern language use in the classroom. Language policies, however, can be instantiated or implemented in out-of-classroom settings. Hence, the current study examines the impact of language policies, in terms of both beliefs and practices, as mechanisms of power in communication between staff members in official meetings taking place at higher education institutions in Saudi Arabia. An online survey taken by 208 members, in addition to semi-structured interviews with eight department chairpersons, revealed that the implementation of monolingual English language policies can minimize the proportion of engagement of staff members, who are less competent in English, in department council meetings, committee meetings, and other official meetings. The data also suggested that the majority of participants in the study believe that multilingual language policies (allowing the use of both Arabic and English) should be avoided in order to not exclude non-Arabic speaking staff members from participation in official dialogue. The study concludes with implications for language policy creation and implementation for out-of-classroom English use in EFL educational institutions.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.002 | 0.007 |
| 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".