On Arabic Language Maintenance Among Arabs Living in Western Countries: A Review of Literature
Bibliographic record
Abstract
Arabic is present in the Arab world and beyond. It is used as an official (or co-official) language in the Arab World (this refers to the twenty-two member states of the Arab League). Also, Arabic has the status of a national language in Mali, Niger, and Senegal. Besides, it is spoken in linguistic enclaves in Nigeria, Cyprus, Turkey, Uzbekistan, Iran, and Afghanistan. In addition, it is employed as the liturgical language in Muslim countries and countries with a Muslim minority all over Africa and Asia. As a result of the migration to Western countries since the end of the nineteenth century, Arabs and Arabic has been present in such countries. The presence of Arabs in Western countries raises the following question: Are these Arabs language maintainers or language shifters? The present article is an attempt to answer this question through reviewing a number of studies that have dealt with Arabic language maintenance among the Arabs living in Western countries (namely the United States of America, the United Kingdom, Canada, Australia, New Zealand, Sweden, Greece, and France), deducing from these studies the major trends in Arabic language maintenance among these Arabs, and providing a critique of the studies.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".