Arabizi Among Kuwaiti Youths: Reshaping the Standard Arabic Orthography
Bibliographic record
Abstract
Arabizi is a trendy language phenomenon utilized by young Arabs to communicate across various social platforms. Young Kuwaitis seem to not be any exception in that regard. This paper aims mainly at investigating the linguistic features of Arabizi as produced by the young generation in Kuwait, and the reasons for which the practice has been persistent amongst the youth community. The main corpus data was collected from spontaneous WhatsApp chats of 35 young Kuwaiti respondents who provided 400 of their e-messages to be linguistically analyzed by the researcher. A digital questionnaire was also implemented to illicit respondents’ responses on the reasons for which young Kuwaitis use Arabizi in their e-messages. Due to the heterogeneity of the spontaneous corpus, supplemental data was provided from a story writing that was sent to the respondents to be re-written in the style they choose when they normally chat on WhatsApp. From a linguistic point of view, the study reveals a number of tendencies that place Arabizi as a unique method of communication with a profile that employs both transcription and transliteration in the way it represents its consonants vs. vowels, Kuwaiti dialectical phoneme shifts and the wide use of extralinguistic features. Intensive code-switching and mixing has also been displayed. The present study also signifies a number of sociolinguistic reasons for which Kuwaiti users of Arabizi employ the script in their e-communication across social platforms.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".