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Record W2724457496 · doi:10.5539/elt.v10n8p11

Unwelcome? English as a Medium of Instruction (EMI) in the Arabian Gulf

2017· article· en· W2724457496 on OpenAlexvenueno aff
Qais S. Ahmadi

Bibliographic record

VenueEnglish Language Teaching · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsMedium of instructionPsychologyArabicClass (philosophy)Qualitative researchAction researchPerspective (graphical)Foreign languagePedagogyMathematics educationSemitic languagesFocus groupLinguisticsSociologySocial scienceComputer science

Abstract

fetched live from OpenAlex

The Qatari college EFL (English as a Foreign Language) classroom is revisited in the author’s second action research study conducted in the small Arabian Gulf country. The literature review allowed the author to gather themes that lead to this groundbreaking inquiry of the residual effects resulting from educational language reforms in the country. Due to the beginning stages of English as a medium of instruction (EMI) in Qatar, to the author’s knowledge, no such research has taken place until now. The qualitative data collection method involved 11 focus group participants from the researcher’s advanced-intermediate IEP (Intensive English Program) class that underwent a semi-structured interview. Results determine that, if given the choice, students would rather receive instruction in the Arabic medium instead of English to pursue undergraduate studies. The author offers additional research questions and recommendations in understanding the student perspective and why the EMI reforms have not been successful.

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)0.007
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 score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0050.006
Scholarly communication0.0060.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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

Citations14
Published2017
Admission routes1
Has abstractyes

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