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Record W4309733291 · doi:10.30853/phil20220632

English-French Language Contacts in Canada: Code Switching and Code Mixing

2022· article· en· W4309733291 on OpenAlex
L. A. Ulianitckaia, Maria Vladimirovna Zlobina

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
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

VenuePhilology Theory & Practice · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsnot available
Fundersnot available
KeywordsLinguisticsCode-mixingCode-switchingComputer scienceLanguage transferMixing (physics)First languageCode (set theory)Neuroscience of multilingualismNoveltyNatural language processingComprehension approachProgramming languagePsychologyNatural languagePhysics

Abstract

fetched live from OpenAlex

The paper describes English-French language contacts in Canada and discusses the main strategies of the modern language policy in relation to the French language in Canada. The approaches to the definition of language interference, code mixing and code switching were studied. The research aims to identify the characteristics of code mixing and code switching in the English-French language pair in written and oral sources in terms of their grammatical expression and functional content. Scientific novelty of the research lies in investigating the reasons for code switching/mixing in the English-French language pair in the situation of state bilingualism in Canada and determining the grammatical and lexical features of code switching/mixing for languages belonging to different language groups, which contributes to the development of language contact theory. The research findings have shown that despite the fact that oral speech is characterised by lack of motivation, spontaneity, emotivity in code mixing and written speech is characterised by motivation and functionality, the grammatical expression of lexical units from the embedded language in the matrix language occurs according to similar principles for oral and written speech.

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)0.004
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.818
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.305
Teacher spread0.287 · 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