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Record W4409519188 · doi:10.5539/ells.v15n2p11

Teach Us English But Without Its Cultural Values

2025· article· en· W4409519188 on OpenAlexvenueno aff
Khalid Al-Seghayer

Bibliographic record

VenueEnglish Language and Literature Studies · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

As English becomes the global lingua franca, the debate intensifies over whether it should be taught it with or without its cultural context, particularly in non-Western countries. Critics fear cultural imperialism and the erosion of local identities, while proponents argue that language and culture are inseparable, and teaching English devoid of its cultural context undermines meaningful communication and intercultural competence. This article explores the arguments for and against cultural integration in English language teaching, backed by recent studies and real-world examples. It also underscores the benefits of culturally responsive teaching, which enhances linguistic proficiency and intercultural understanding while safeguarding local identities. Practical strategies for balancing cultural sensitivity with global communication needs are proposed, emphasizing the importance of fostering intercultural competence. The article concludes by asserting that teaching English rooted in its cultural context is essential for preparing learners to navigate effectively in an interconnected world.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.013
Scholarly communication0.0070.004
Open science0.0000.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.002

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 head0.024
GPT teacher head0.429
Teacher spread0.405 · 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 designNot applicable
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

Citations10
Published2025
Admission routes1
Has abstractyes

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