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Record W1914020546 · doi:10.5539/ass.v11n23p83

The Evaluation of Iranian EFL Learners’ Interlanguage Pragmatic Knowledge through the Production of Speech Acts

2015· article· en· W1914020546 on OpenAlexvenueno aff
Kim Hua Tan, Atieh Farashaiyan

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsInterlanguagePsychologyLinguisticsSpeech actPragmaticsConceptualizationProduction (economics)Politeness

Abstract

fetched live from OpenAlex

This study evaluated Iranian intermediate EFL learners’ knowledge of interlanguage pragmatic learning outcomes through the production of the speech acts of apology, request, and refusal. The study drew upon the conceptualization of Austin (1962) speech act theory and Brown and Levinson (1987) politeness theory as the theoretical framework of this study. The participants of the study included 235 EFL intermediate learners. Discourse Completion Task (DCT) was used as the instrument for data collection. The results of the data from the tests on speech acts showed that learners utilized more conventional or routinized strategies to perform these speech acts. Moreover, the results revealed that there were not so much differences in the frequency, shift and type of speech acts strategies or semantic formulas utilized in the production of speech acts by Iranian EFL learners in responding to a higher, an equal, and a lower-status person. The results also were suggestive of the learners’ lack of pragmalinguistic and sociopragmatic knowledge. The implication of this study is for language teachers to teach interlanguage pragmatics explicitly in EFL contexts to draw learners’ attention to both pragmalinguistic and sociopragmatic features, pay more attention to these areas and allocate more time and practice to solve learners’ problems in these areas.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.879
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0000.001
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 head0.122
GPT teacher head0.386
Teacher spread0.264 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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