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Record W4285726586 · doi:10.5430/wjel.v12n6p147

Exploring EFL Learners’ Intercultural Sensitivity and Communication Apprehension

2022· article· en· W4285726586 on OpenAlexvenueno aff
Chaleomkiet Yenphech, Suphakit Phoowong, Somyong Som-In, Sittisak Pongpuehee, Jariyaporn Amatiratna, Kampeeraphab Intanoo

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsApprehensionCommunication apprehensionIntercultural communicationPsychologySensitivity (control systems)AnxietySocial psychologyPedagogyCognitive psychologyEngineering

Abstract

fetched live from OpenAlex

This study aimed to investigate the average level of intercultural sensitivity and communication apprehension of EFL teaching assistants learners. In addition to propose the correlation between intercultural sensitivity and communication apprehension. In this study, the researchers selected and applied the mixed-method research paradigm. According to the findings of the research, intercultural sensitivity and communication apprehension have some positive effects on the performance of EFL teaching assistants (TA). The correlation and recognition can be measurably important in the intercultural sensitivity and communication apprehension for social contrasts in the temporary workplace with a high level. Proposing collaboration and adaptability in intercultural sensitivity and communication apprehension with EFL teaching assistants (TA) in order to modify negative respect, enjoyment, and open-mindedness caused by cultural differences. This furthermore encompasses the ability to deal with the tension and anxiety that comes with situations characterized by cultural variances.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.063
GPT teacher head0.311
Teacher spread0.248 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2022
Admission routes1
Has abstractyes

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