The Relationship Between English Language Proficiency and Politeness in Making Requests: A Case Study of ESL Saudi Students
Bibliographic record
Abstract
The acquisition of language as well as the acquisition of social consideration, or politeness, are two sets of separate skills. However, the lack of language knowledge by an English language learner can result in social mistakes that can be perceived as impolite by native speakers. The present study aimed to explore the relationship between English language proficiency and politeness in making requests at retail shops, specifically focusing on Saudi students in the United States. There were five participants in this study: two Saudi ESL students with low English proficiency; two Saudi Ph. D. students with advanced English proficiency, and an American waiter who speaks English as a native language. The study was conducted by using a qualitative research method in two phases. The first phase included observing all participants during interaction, and the second phase included interviewing a server at one of the restaurants where the students visited. The main findings revealed that there was no strong correlation between politeness and English language proficiency in making requests. However, there were some factors found in this study that contributed to politeness level, such as intonations and the use of politeness markers.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".