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Record W4284893010 · doi:10.5430/wjel.v12n6p59

The McChicken Phenomenon: How Has English Become a Prevalent Language among Kuwaiti Youths?

2022· article· en· W4284893010 on OpenAlexvenueno aff
Noor A. Hayat, Yousuf B. AlBader

Bibliographic record

VenueWorld Journal of English Language · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenonEntertainmentCultural phenomenonSocial phenomenonArabicEveryday lifePsychologyLinguisticsSociologyPolitical scienceSocial scienceEpistemologyLaw

Abstract

fetched live from OpenAlex

The worldwide influence of the English language is undeniable, given its common use in various forms of international entertainment, social media, the Internet, and so on. English has affected many countries’ languages, and Kuwaiti Arabic is no exception, as evidenced by the so-called “McChicken” phenomenon. This phenomenon refers to the widespread use of English by young Kuwaitis who have low proficiency in Kuwaiti Arabic. However, despite the everyday use of the term in colloquial speech, the phenomenon has remained underexplored. In light of the above, this study investigates the development of the “McChicken” phenomenon. Through an online questionnaire survey and audio-recorded interviews, this study uncovers how Kuwait’s young generation came to rely heavily on code-switching in their communication. This study makes a significant contribution to the literature because it sheds light on this pervasive and rapidly spreading, yet barely understood phenomenon. Further, this study delves into a topic that highlights cross-cultural identities and perspectives.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.073
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0040.003
Open science0.0010.003
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 head0.033
GPT teacher head0.345
Teacher spread0.312 · 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 designObservational
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

Citations6
Published2022
Admission routes1
Has abstractyes

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