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Record W4407673408 · doi:10.1017/cnj.2024.32

Investigating attitudes towards a changing use of anglicisms in Quebec French

2024· article· en· W4407673408 on OpenAlexafffundabout
Marie-Ève Bouchard

Bibliographic record

VenueThe Canadian Journal of Linguistics / La revue canadienne de linguistique · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLinguisticsPsychologyPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

Abstract This article investigates speakers’ attitudes towards the use of anglicisms in Quebec French, particularly verbs. The common practice in Quebec French is to morphologically integrate these anglicisms into the French language (e.g., il m'a ghosté ). However, in recent years, some French-speaking Quebecers have been using the unintegrated forms (e.g., il m'a ghost ). This change of practice for the use of English-origin verbs is a linguistic innovation that is emerging from young French speakers in the Montreal area (M-E Bouchard 2023a). To investigate attitudes towards the use of anglicisms (integrated and non-integrated forms), 675 French-speaking Quebecers were asked the following open-ended question: Do you have anything to say about the use of anglicisms in Quebec French? The current study consists of a qualitative analysis of the participants’ answers to this specific question. This study has found evidence of linguistic hierarchies between the use of anglicisms that are morphologically integrated and those that are not. The anglicisms that are not morphologically integrated into the French language are perceived by many participants as incorrect and as challenging communication and understanding between users and non-users of the unintegrated forms. Participants associate the use of the unintegrated forms with young people.

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 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)0.002
metaresearch head score (Gemma)0.112
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.872
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.112
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
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 head0.036
GPT teacher head0.243
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 teacher head, not a consensus.

Study designNot applicable
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

Citations2
Published2024
Admission routes3
Has abstractyes

Explore more

Same venueThe Canadian Journal of Linguistics / La revue canadienne de linguistiqueSame topicLinguistics, Language Diversity, and IdentityFrench-language works237,207