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Record W4409004467 · doi:10.1080/15348458.2025.2469086

Discourses of Language Endangerment and Maintenance Among Young Bi/Plurilingual Speakers of a Francophone Minority School in Vancouver

2025· article· en· W4409004467 on OpenAlexafffundabout
Marie-Eve Bouchard

Bibliographic record

VenueJournal of Language Identity & Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsFrenchLinguisticsMinority languageSociologyPsychologyPhilosophy

Abstract

fetched live from OpenAlex

This article examines the ways that discourses of endangerment and maintenance have been taken up among young bi/plurilingual speakers who attend a French-language high school in Vancouver. It is based on interviews conducted with 31 grade-11 students (16–17 years old). A thematic analysis of the data revealed two main contrasting discourses among students: one in which French is perceived as being threatened by English (a discourse of endangerment) and one in which French is valued as linguistic capital and viewed as a component of their identities (a discourse of maintenance). These dominant discourses are comprised of five different themes. The findings suggest that these young Vancouverites have internalised common discourses about the endangerment of French, but that they are also mobilising new discourses of maintenance that involve different ideologies about the continuing use of French within the community, which calls for a redefinition of what it means to be a French-speaker in Vancouver.

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)0.004
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 score0.322
Threshold uncertainty score0.647

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0190.008
Scholarly communication0.0060.001
Open science0.0010.004
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 head0.009
GPT teacher head0.388
Teacher spread0.380 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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