Can Language Show-off Promote Social Status and Solidarity? An Explanatory Study of the Cognitive Attitudes of Kuwaitis towards Arabic-English Code-switching in Kuwaiti Social Domains
Bibliographic record
Abstract
The omnipresence of Arabic-English code-switching in Kuwaiti social contexts is unequivocal. Several studies have indicated that the motivation behind deploying such linguistic variety is to promote social status and solidarity. This study investigates whether adopting such linguistic variety in Kuwaiti social domains meets code-switchers’ expectations by characterizing and positioning them in the desired social category. Using a verbal-guise test, the study examines the status (class, education, intelligence) and solidarity (showing-off, attractiveness, sociability) dimensions of 92 Kuwaitis’ cognitive attitudes towards Arabic-English code-switching. A paired t-test has shown that Kuwaitis’ attitudes are in favour of Arabic-English code-switching. In complete contrast with other studies, a one-way ANOVA has uncovered that older generations are more in favour of code-switching than younger ones. Additionally, the results suggest that females are less in favour of Arabic-English code-switching than males, and their positive ratings for Kuwaiti Arabic are significantly higher. The findings are subsequently examined and subjected to critical analysis in order to elucidate the extent to which this phenomenon is deemed appealing by specific parts of Kuwaiti society whilst unfavoured by others. The paper concludes with some recommendations for future research endeavours that might contribute to the investigation of language attitudes and variation in Kuwait.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".