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Bringing Cross-Cultural Communication Analysis Into Foreign Language Classrooms

2019· book-chapter· en· W2912707796 on OpenAlexaff
Trudy O’Brien

Bibliographic record

VenueAdvances in educational technologies and instructional design book series · 2019
Typebook-chapter
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCarleton University
Fundersnot available
KeywordsIntercultural communicationCross-cultural communicationContext (archaeology)MulticulturalismForeign languageConfusionLinguisticsIntercultural relationsPedagogyPsychologyCommunicationGeography

Abstract

fetched live from OpenAlex

The teaching of a second or foreign language has always incorporated some aspect of cultural information, but the full and rather complex nature of cross-cultural and intercultural communication has not always been an explicit pedagogical focus. The chapter outlines the key components of cross-cultural and intercultural communication (CCC/ICC), and reviews some major theories that have dominated the area. It is suggested that providing explicit instruction in CCC/ICC to language learners will prepare them for interacting appropriately in the target language in whatever global context they may wish to use it. Learners need to be not only linguistically and pragmatically but culturally competent as well as they move into multicultural contexts of interaction in that language. Specific elements of cross-/intercultural communication with regards to linguistic features and potential points of confusion in the EFL (English-as-a-foreign language) classroom are discussed as accessible examples. The chapter then relates some ways that cross-/intercultural mindfulness and understanding can form an active part of the teaching of a second/foreign language in order to enhance the full language learning experience and subsequent entry to successful communication.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.284
Teacher spread0.266 · 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.

Study designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations4
Published2019
Admission routes1
Has abstractyes

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