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Record W2767935057

An Interlanguage Pragmatic Study of Saudis’ Complaints

2017· article· en· W2767935057 on OpenAlexvenueno aff
Nader Muhaya Rashidi

Bibliographic record

VenueStudies in literature and language · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsFalse accusationBlamePsychologyComplaintPolitenessAnnoyanceSocial psychologyLinguisticsSpeech actAudiologyPolitical scienceMedicine
DOInot available

Abstract

fetched live from OpenAlex

This study investigated the strategies monolingual Saudi Arabian adults (MSAAs), Saudi EFL adult learners (SEFLALs), and native speakers of English (ENSs) used when complaining. Another related aim was investigating whether SEFLALs displayed pragmatic transfer when using complaint strategies. A total of 183 written responses were collected from MSAAs, SEFLALs, and ENSs via a three-item discourse completion task (DCT) were analyzed. Findings revealed the strategies used by the study participants when performing the speech act of complaints. First, hints, request and annoyance were the most frequently used strategies by MSAAs, SEFLALs, and ENSs. Second, there were no statistically significant differences among MSAAs, SEFLALs, and ENSs in using the strategy of direct accusation which consistent with the concept of positive pragmatic transfer. Third, hints, behavioral blame, request and indirect accusation were cases of weak negative pragmatic transfer as employed the SEFLALs in the current study. Fourth, modified blame was consistent with concept of strong negative pragmatic transfer. Finally, the last two strategies; annoyance and threat were consistent with no transfer, that is, SEFLAL employed these two strategies as ENSs.

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)0.000
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 score0.055
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.055
GPT teacher head0.392
Teacher spread0.337 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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