The Implications of Global English for Language Endangerment and Linguistic Identity: The Case of Arabic in the GCC States
Bibliographic record
Abstract
Numerous sociolinguistic studies have been concerned with investigating the factors that pose challenges to the position of Arabic in the Arab Gulf countries including the demographic structure, migrant labor, bilingual education, and the unique diaglossic nature of Arabic. However, thus far, there has been no conceptual framework for addressing the implications of the increasing use of English for the position and future of Arabic in these countries. A number of studies concluded that English has superseded Gulf Arabic and dominated the linguistic identity of its native speakers without providing empirical evidence for such claims. In the face of this limitation, this study adopts a sociolinguistic framework using language planning and language policy (LPP) methods in order to investigate the effects and implications of the use of English as a global language and lingua franca in the Arab Gulf states and propose workable, reliable and effective language policies that can help in maintaining Arabic as the first language in the Gulf Cooperation Council (GCC) states and addressing problems of language endangerment and death. Results indicate that the disappearance of a language and the loss of its status cannot be solely attributed to the widespread of global English. Global English, on the contrary, should not be considered as a threat to the linguistic and national identity in the GCC countries. The real threat that Arabic faces is the failure to meet the increasing needs of its users and speakers which has its implications for the status and future of Arabic. It is suggested then that more descriptive approaches should be adopted in the analysis and teaching of Arabic. Linguistic changes of Arabic should be considered inevitable and not be resisted in order for Arabic to address the changing needs of its users. Arabic should also be more involved in today’s globalised world. Finally, the sense of linguistic identity should be promoted among citizens and students.
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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 | 0.001 |
| Science and technology studies | 0.018 | 0.016 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".