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Record W2060987205 · doi:10.5539/elt.v5n11p118

A Comparative Study of the Complaint Strategies among Iranian EFL and ESL Students – The Study of the Effect of Length of Residence and the Amount of Contact

2012· article· en· W2060987205 on OpenAlexvenueno aff
Marzieh Mofidi, Zohreh Gooniband Shoushtari

Bibliographic record

VenueEnglish Language Teaching · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyComplaintInterlanguageCompetence (human resources)ResidencePolitenessWillingness to communicateCommunicative competenceMathematics educationPedagogyLinguisticsSocial psychology

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the pragmatic transfer Iranian EFL and ESL learners of English showed when complaining in English. The study aimed to find out if there exists any relationship between the amount of contact with English and pragmatic competence of both EFL and ESL groups as well as the relationship between the duration of stay in English-speaking countries and the pragmatic competence of complaining in the ESL group, living and studying abroad for some years. For this purpose, the data were gathered from four groups: twenty Iranian native speakers of Farsi and twenty English native speakers of English, as the baseline groups, as well as twenty EFL and twenty ESL learners, as the interlanguage data groups. The data were elicited through a personal information form and a DCT in which they were asked to answer six imaginative situations including complaint-required situations. The DCT data were examined to see to what extent the time spent with English and the duration of stay in target countries and the pragmatic competence of EFL and ESL respondents were related. The findings revealed no significant relationship between the amount of contact, the time spent abroad and the pragmatic competence of Iranian EFL and ESL learners.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.022
GPT teacher head0.311
Teacher spread0.289 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2012
Admission routes1
Has abstractyes

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