Loanwords in the Urban Meccan Hijazi Dialect: An Analysis of Lexical Variation according to Speakers’ Sex, Age and Education
Bibliographic record
Abstract
This study aims to investigate the degree of lexical variation in Urban Meccan Hijazi Arabic (UMH) by identifying the loan words that are commonly spoken in this dialect and determining from which languages they have been borrowed. Also, it explores the effect of social factors such as age, sex and educational level on the use of loan words by UMH speakers. For the purpose of the study, I designed a questionnaire and distributed it to eighty participants whose mother tongue is UMH. The sample consisted of three groups, namely, old and young, male and female and educated and uneducated participants. In order to provide answers to the research questions, the questionnaire was divided into two sections; section one investigates the participants’ background, i.e., their age, sex, educational level, how long have they been living in Mecca, etc., and section two examines their use of the loan words in UMH. The results revealed that in addition to some words that have been borrowed from Persian and Italian, most of the loan words found in UMH have been borrowed from Turkish. This could have resulted from the Ottoman occupation of Saudi Arabia for around 400 years, and the interaction with pilgrims who visit Mecca every year. Additionally, the results of the t-tests showed that the differences between the three groups (i.e., old vs. young, male vs. female and educated vs. uneducated) are statistically significant. This indicates that the three social factors play a crucial role in the participants’ use of the loan words in UMH. Finally, the study concludes with some recommendations for further research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".