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Record W2475168131 · doi:10.5430/elr.v5n2p57

Investigating Code Switching between Arabic/English Bilingual Speakers

2016· article· en· W2475168131 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueEnglish Linguistics Research · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsCode-switchingArabicConversationLinguisticsPerspective (graphical)Context (archaeology)Code (set theory)Modern Standard ArabicNeuroscience of multilingualismComputer sciencePsychologyArtificial intelligenceHistoryProgramming language

Abstract

fetched live from OpenAlex

This study investigates code switching in Arabic/English bilingual speech. The data analysed in this paper is an interview between two female Arabic participants in the context of hair and skin care. It took place in Dubai, United Arab Emirates, where Arabic is the official language and English is used as a second language. The paper studies the occurrences of code switching from Arabic to English in the conversation from a sociolinguistic perspective. A conversational analysis is carried out in an attempt to understand functions of code switching based on the participants' turns. The findings show that switching from Arabic to English is overwhelmingly utilized in the interview. It is argued that code switching occurs mostly in elaboration. Additional functions of code switching by speakers include grabbing the audience attention, emphasizing points, and showing knowledge of topic-related terminologies.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.546
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.938
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.546
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.243
GPT teacher head0.533
Teacher spread0.290 · 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