Gender Differences in the Use of Negation Markers in Qassimi Arabic: A Sociolinguistic Analysis
Bibliographic record
Abstract
This study investigates the use of negation markers in the Qassimi Arabic (QA), a variety of Najdi Arabic, spoken in central-north Saudi Arabia. Employing a qualitative approach, we observed the speech patterns of 28 Qassimi speakers (14 male, 14 female; ages 28–34). The local negators Muhub/Mub were used by 39% of males but only 12.4% of females. Conversely, the supralocal negator Mu was used by all females (35.6%) and a smaller portion of males (12.9%). These findings highlight gender differences in negation marker preference within the Qassim dialect. The study aims to improve communication between genders and encourage broader understanding of linguistic variation in Najdi Arabic.
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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 | 0.001 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".