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Record W2954746266 · doi:10.5539/ijel.v9n4p241

The Implementation of Communication Strategies to Exchange and Negotiate Meanings in a Simulation of Job Interview

2019· article· en· W2954746266 on OpenAlexvenueno aff
Steffie Mega Mahardhika, Dwi Rukmini, Abdurrachman Faridi, Januarius Mujiyanto

Bibliographic record

VenueInternational Journal of English Linguistics · 2019
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsNegotiationIndonesianJob interviewPsychologyConversationComprehensionPropositionIntercultural communicationSocial psychologyDyadReinterpretationFeelingLinguisticsPedagogySociologyCommunication

Abstract

fetched live from OpenAlex

Students of English as a foreign language find it difficult to initiate a talk and exchange meanings in an interaction. They find it hard to break the iceberg and act by asking, commanding and answering to make the speakers or listeners’ intention fulfilled. As such they do not have medium to exchange experiences. The present study aims to explain the way meanings are negotiated and exchanged in the implementation of communication strategies in a simulation of a job interview. The subjects of this qualitative study are students of Indonesian Vocational College. The object of the study is the students’ utterances in the interview. Functional semantic reinterpretation of turn-constructional units of conversation analysis is implemented to explain how the students exchange and negotiate meanings. The findings suggest that the realization of exchange and negotiation of meanings is influenced by moves assigned to speech functions classes and the types of meanings implemented in the act of production and comprehension of the whole communication. The subjects of the present study can be categorized into productive speakers. The students negotiate feelings and attitudes more than that of the content of the proposition.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.752
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0000.000
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 head0.040
GPT teacher head0.354
Teacher spread0.313 · 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 designTheoretical or conceptual
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

Citations1
Published2019
Admission routes1
Has abstractyes

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