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Record W2295742161 · doi:10.1080/15348458.2015.1137476

“I Am Not a Francophone”: Identity Choices and Discourses of Youth Associating With a Powerful Minority

2016· article· en· W2295742161 on OpenAlexafffundabout
Cynthia Groff, Annie Pilote, Karine Vieux-Fort

Bibliographic record

VenueJournal of Language Identity & Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité Laval
FundersUniversité Laval
KeywordsIdentity (music)Context (archaeology)SociologyFrenchFocus groupGender studiesQualitative researchDiscourse analysisFocus (optics)LinguisticsPsychologySocial scienceAnthropology

Abstract

fetched live from OpenAlex

Taking a broad interest in the linguistic, educational, and identity issues relevant to young people, this article examines the experiences and discourses of linguistic minority youth in the French-dominant context of Québec City. Our analysis is based on qualitative interviews conducted with 10 young people who speak a language other than French at home and who chose to study in English at the postsecondary level. Beyond exploring the local impact of language policies, we focus on the identity choices these youth make in positioning themselves and the discourses that they appropriate in describing their sociolinguistic context. Findings suggest that tensions between linguistic groups in Québec are perpetuated through discourses that distance one group from another, including discourses of closed-mindedness and superiority. What the students in our study appear to be doing is rejecting a minority identity by invoking national and international scales through their discourses, scales in which English is dominant.

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)0.008
metaresearch head score (Gemma)0.007
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 score0.491
Threshold uncertainty score0.976

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0250.015
Scholarly communication0.0090.004
Open science0.0020.006
Research integrity0.0020.004
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 head0.021
GPT teacher head0.411
Teacher spread0.390 · 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

Citations16
Published2016
Admission routes3
Has abstractyes

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Same venueJournal of Language Identity & EducationSame topicMultilingual Education and PolicyFrench-language works237,207