Kuwaitis’ Attitudes towards Vehicles’ Stickers in Kuwait: A Sociolinguistic Investigation
Bibliographic record
Abstract
Language attitudes cover a wide variety of emphases, and the reasons for studying language attitudes attract sociolinguists. Language attitudes may well tackle issues extend to all sociolinguistic and social psychological phenomena, such as how we locate ourselves socially and how we relate to other individuals and groups. They may also shape our behaviors and experiences. This study investigates Kuwaitis’ Attitudes towards vehicles’ stickers in Kuwait. Data were collected from responses to 17 items - questionnaire and 1 open-ended question aimed at investigating Kuwaitis’ attitudes towards the content (political, religious, aesthetic, etc. and the shape (size, color, etc.) of vehicles’ stickers. Findings were analyzed statistically. Means, Standard deviations, T-tests, and ANOVA were utilized. Results show that Kuwaitis, in general expressed negative attitudes towards both the content and the shape of the stickers. The open-ended question data provided inclusive data as to the reasons behind such a negative attitude.
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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 | 0.003 |
| 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 | 0.000 |
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".