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Record W2915453002 · doi:10.5539/ijel.v9n2p89

The Relationship Between English Language Proficiency and Politeness in Making Requests: A Case Study of ESL Saudi Students

2019· article· en· W2915453002 on OpenAlexvenueno aff
Abdullah Alshakhi

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPolitenessPsychologyInterviewLanguage proficiencyLinguisticsEnglish languageFirst languageQualitative researchMathematics educationSociology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.022
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.022
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.057
GPT teacher head0.379
Teacher spread0.322 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2019
Admission routes1
Has abstractyes

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