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Teaching English: a transition from a monolithic to a pluricentric approach

2025· article· en· W4408464001 on OpenAlexaboutno aff
Zarrina Salieva, Nasir Ahmad Tayid, Abdul Bari Rahmany

Bibliographic record

VenueЗарубежная лингвистика и лингводидактика · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsTransition (genetics)LinguisticsComputer scienceMathematics educationPsychologyPhilosophyChemistry

Abstract

fetched live from OpenAlex

English language teaching (ELT) has evolved significantly over the past century. In the early 20th century, the dominant model of language teaching was based on a monolithic approach, where the focus was primarily on a single, standardized version of the language. This approach assumed that a uniform language was essential for effective communication and international understanding (Crystal, 2003; Richards & Rodgers, 2014). The methodology was largely rooted in structuralist theories of language and was heavily influenced by the direct method of language teaching, which emphasized speaking and listening over grammar or translation (Richards & Rodgers, 2014). During this period, English teaching focused on producing "native-like" speakers, often modeled after the language standards of the United Kingdom or the United States (Clyne, 1992). However, as the global use of English expanded, particularly with the rise of World Englishes and the increasing use of English in countries outside of the traditional Anglophone world, the monolithic approach began to be questioned. By the late 20th century, scholars and educators began advocating for a more inclusive and flexible approach, one that acknowledged the diverse ways in which English was spoken and used across different regions and cultures (Gerhard, 1992; Xie, 2014). This shift towards embracing the diversity of English use around the world led to the development of the pluricentric approach, which recognizes the legitimacy of multiple English varieties and their role in shaping language learning and communication (Sharifian, 2014). The pluricentric approach to language teaching gained traction with the growing recognition that English, as a global lingua franca, is spoken in many different ways, often influenced by local languages, cultures, and social contexts (Hutz, 2022). As Kachru (1992) argued in his model of the three circles of English, the language cannot be reduced to one standardized form, as it exists in multiple forms that each serve different communicative purposes. This model divides the global spread of English into three concentric circles: the Inner Circle (where English is the native language, such as in the UK, US, and Canada), the Outer Circle (where English is a second language, institutionalized in countries like India, Nigeria, and Singapore), and the Expanding Circle (where English is learned as a foreign language, as in countries like China and Japan). This framework illustrates the shifting perspectives on English, from a single language norm to a pluricentric phenomenon with regional varieties that each have their own validity and significance (Clyne, 1992; Kachru, 1992). The move from a monolithic to a pluricentric approach has far-reaching implications for language teaching. It reflects the growing importance of linguistic diversity in education and acknowledges the need for language learners to engage with English in its various global forms. This article will explore the historical development of English language teaching, analyze the shift from the monolithic to the pluricentric approach, and discuss the pedagogical implications of embracing multiple varieties of English in contemporary ELT.

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)0.010
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0060.015
Scholarly communication0.0090.010
Open science0.0020.013
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0020.001

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 head0.012
GPT teacher head0.218
Teacher spread0.207 · 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 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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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