Makkan Arabic in the digital age: A sociolinguistic analysis of the representation of fricative, stop, and sibilant variation in WhatsApp text messages
Bibliographic record
Abstract
This study examines Makkan Arabic speakers’ orthographic representation of standard and colloquial variants in their WhatsApp text messages. In particular, we examine the role that speaker gender, speaker age, gender composition of conversations, and topic of discussions play in Hadari Makkans’ representation of standard and colloquial variants of the variables (th), (dh), and (Dh). Statistical analyses reveal that women favor colloquial variant stops [t] and [d], while men exhibit a preference for standard variants [θ] and [ð], particularly when conversing with other men. For (Dh), however, both women and men favor the standard variant [ðˤ]. Age also plays a role in the distribution of variants, with speakers favoring standard variants as they age. The use of fricatives [θ] and [ð] also increases when participants discuss formal topics, which suggests an implicit association between standard language and formality, despite the inherent informality of WhatsApp interactions. This study provides insights into how phonological variation is orthographically represented within a written genre designed to mimic spontaneous conversation and enriches the broader discourse on Arabic language variation and digital communication.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".