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Record W4409441534 · doi:10.1111/josl.12704

“You Speak Well for an Anglophone”: Resisting the Processes of Delegitimation and Developing Linguistic Security

2025· article· en· W4409441534 on OpenAlexafffundabout
Marie‐Eve Bouchard

Bibliographic record

VenueJournal of Sociolinguistics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsLinguisticsSociologyPhilosophy

Abstract

fetched live from OpenAlex

ABSTRACT This article explores the ways that young French speakers from British Columbia, an English‐dominant province of Canada, navigate different processes of linguistic delegitimation and how these processes are linked to linguistic insecurity. The findings are derived from interviews conducted with nine young French speakers from British Columbia who shared their most significant experiences of linguistic insecurity. The results, which are based on a thematic analysis, show how participants are being delegitimized by family members from dominant French‐speaking contexts due to some of their linguistic practices that diverge from standard forms. They also indicate that participants have found ways to gain linguistic security by acquiring knowledge, participating in their communities, and accepting different linguistic practices that characterize their variety of French. However, this linguistic security work is limited by the existing ideologies in the French‐speaking world that delegitimize the non‐standard varieties.

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)0.006
metaresearch head score (Gemma)0.009
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 score0.301
Threshold uncertainty score0.599

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0160.019
Scholarly communication0.0080.002
Open science0.0010.005
Research integrity0.0020.003
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 head0.072
GPT teacher head0.471
Teacher spread0.399 · 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

Citations2
Published2025
Admission routes3
Has abstractyes

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