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Record W2577282329 · doi:10.5539/ijel.v7n2p32

Impoliteness in the Realization of Complaint Speech Acts: A Comparative Study of Iranian EFL Learners and Native English Speakers

2017· article· en· W2577282329 on OpenAlexvenueno aff
Atefeh Nikoobin, Mohsen Shahrokhi

Bibliographic record

VenueInternational Journal of English Linguistics · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsSarcasmPsychologyComplaintSocial distanceSpeech actSignificant differenceSocial psychologyTest (biology)LinguisticsIronyPolitical science

Abstract

fetched live from OpenAlex

This study was conducted to investigate the impolite complaint strategies that are used by Iranian EFL learners and native speakers in relation to social distance. This study also aimed at determining if there were significant differences among the strategies used by each group and if there was a significant difference between Iranian native speakers of English. To this end, 40 Iranian EFL learners and 20 Americans who were native speakers of English participated in this study. To make sure about the homogeneity of Iranian participants the Oxford Placement Test (OPT) was conducted. A questionnaire containing 12 different situations was designed by the researchers and was given to the participants to express their complaints for each situation. The results revealed that there were significant differences among the strategies used by each group; the most common strategy that was used by both groups of participants was positive impoliteness and the least common one was bald-on-record. Although the most and least common strategies used by both groups were the same, Iranians had a stronger tendency for using sarcasm in low social distance situations while natives had a stronger tendency for using bald-on-record in high social distance contexts. This study has implications for EFL curriculum designing in Iran and can make Iranian EFL instructors familiar with the importance of impoliteness as an indispensable part of language.

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)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.323
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
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 head0.081
GPT teacher head0.367
Teacher spread0.287 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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