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Record W1559599257 · doi:10.5539/ass.v11n18p55

Language Idiosyncrasies in Second Language Learners’ Use of Communication Strategies

2015· article· en· W1559599257 on OpenAlexvenueno aff
Suryani Awang, Marlyna Maros, Noraini Ibrahim

Bibliographic record

VenueAsian Social Science · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMalayContext (archaeology)PremisePsychologySecond languagePsychological interventionPoint (geometry)LinguisticsPersonalityComputer scienceSocial psychology

Abstract

fetched live from OpenAlex

The term, “language idiosyncrasies” can generally be defined as what an individual typically says that becomespart of his or her personality. In doing so, this language behaviour can either positively or negatively impactcommunication. Based on this premise, the current study sets out to examine the occurrences of languageidiosyncrasies in second language (L2) communication, particularly at the point when communication strategies(CSs) were employed to overcome communication problems. By employing non-participant observations of realuniversity admission interviews, this study departs from the other studies related to CSs which involvednon-authentic, simulated environments. The observed interview sessions were conducted in the English languageinvolving 29 Malay candidates from 20 interview sessions. These sessions were video-recorded before the rawdata were transcribed. The results revealed some occurrences of language idiosyncrasies in candidates’utterances hence, supporting Paribakht’s (1985) research finding that speakers exhibited idiosyncratic patterns inthe realization of communication strategies. The results also concurred with the findings of past studies on theinfluence of speakers’ L2 proficiency level on their use of CSs. As all participants were Malays communicatingin an English speaking context, issues on cultural values added to the richness of the data and will also bediscussed. While the findings may not be generalizable to all populations, it is hoped that this study will informcurriculum developers of language idiosyncrasies of Malay students, who form the bulk of the studentpopulation in Malaysia, so that awareness may be raised and appropriate interventions may be introduced.

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)0.000
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: none
Teacher disagreement score0.532
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0010.002
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 head0.070
GPT teacher head0.329
Teacher spread0.259 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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