Influence of English and French on Arabic Dialects: A Sociolinguistic Study of Algeria, Morocco, Jordan, and the UAE
Bibliographic record
Abstract
This study investigates language borrowing with a specific focus on the incorporation of linguistic elements from French and English into Arabic dialects spoken in Morocco, Algeria, Jordan, and the UAE. These countries were selected due to their representative geographical distribution across the Arab world and their historical exposure to English and French. The research aims to identify the primary uses and functions of borrowed words and the reasons behind this linguistic phenomenon. A sociolinguistic perspective was employed to examine how speakers contribute to language change through borrowing. To achieve this, four groups of undergraduate students from the selected countries participated in an unstructured questionnaire, which they completed using their knowledge or through the top Arabic newspapers from their respective countries. Borrowed words were categorized by usage, function, source language, and reason for borrowing. The findings showed that most borrowed words from both French and English served technical or educational functions. The primary reasons for language borrowing were related to post-colonial linguistic influences and the need to adapt to globalization, especially in education and modern communication systems. This study underscores the role of language borrowing in shaping contemporary Arabic dialects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".