Linguistic Diversity in Oman: Analyzing the Influence of Gender and Local Omani Languages on Arabic Writing Proficiency Among Omani Students
Bibliographic record
Abstract
The Sultanate of Oman presents a unique case, given its rich linguistic community, primarily composed of several local languages distinct from Arabic, which people acquire first. However, limited studies have explored the effect of local Omani languages and gender on linguistic errors in writing in Arabic as a second language by Omani undergraduate students at one of the public universities in the Sultanate of Oman. Therefore, this study aims to analyze linguistic errors in writing in Arabic as a second language by Omani undergraduates. A qualitative research design was employed in analyzing the writings of 20 students: 10 male and 10 female students. Two taxonomies were utilized to comprehensively describe all grammatical errors and their types. The findings revealed gender similarities in terms of committing most of the errors in the three top categories: spelling, linguistic structures, and syntax. However, the variations in error types highlight gender differences in linguistic errors, with male students primarily making addition errors, whereas female students primarily make deletion errors. Furthermore, it was found that writing in Arabic as a second language can be influenced by certain local Omani languages. In other words, students whose L1 is similar to Arabic made fewer mistakes compared to those whose L1 differs from Arabic, thus implying the positive transfer of L1. These results suggest the significance of gender and local languages in acquiring and learning a second language, which could be employed pedagogically in varied contexts.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".