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Record W2795161244 · doi:10.5539/ach.v10n2p1

Communicative Language Teaching (CLT) in EFL Context in Asia

2018· article· en· W2795161244 on OpenAlexvenueno aff
Liping Wei, Hsin‐Hui Lin, Freddie W. Litton

Bibliographic record

VenueAsian Culture and History · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsCurriculumContext (archaeology)Communicative language teachingChinaMathematics educationPedagogyEnglish as a foreign languageSociologyForeign languagePsychologyLanguage educationPolitical scienceGeographyLaw

Abstract

fetched live from OpenAlex

This paper provides an in-depth investigation into the application of Communicative Language Teaching (CLT) in English as a Foreign Language (EFL) context in Asia, and China in particular. It reveals that CLT has not been fully acknowledged and espoused by Asia’s English language educators at the classroom level. Additionally, it unpacks the various factors that have impeded educators in Asia from enacting CLT. Through introducing the concepts of “teacher as curriculum implementer” and “teacher as curriculum maker,” it brings to surface why a mandated curriculum change as CLT cannot be realized in EFL context in Asia. The paper argues that teachers should be constructors rather than merely receivers of the imposed pedagogical reforms. The top-down educational enterprise of implementing CLT cannot succeed unless it is embraced by teachers with their reconfigurations in light of their specific teaching situations.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0040.003
Open science0.0010.006
Research integrity0.0010.002
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 head0.026
GPT teacher head0.254
Teacher spread0.229 · 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 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

Citations25
Published2018
Admission routes1
Has abstractyes

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