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Record W2042383189 · doi:10.1080/13670051003787566

The sociolinguistic competence of former immersion students at the post-secondary level: the case of lexical variation

2010· article· en· W2042383189 on OpenAlexaffabout
Katherine Rehner

Bibliographic record

VenueInternational Journal of Bilingual Education and Bilingualism · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLinguistic Variation and Morphology
Canadian institutionsUniversity of TorontoAmgen (Canada)
Fundersnot available
KeywordsFrench immersionPsychologyFrenchCompetence (human resources)Neuroscience of multilingualismAP French LanguageLinguisticsImmersion (mathematics)PedagogySociologyForeign languageSocial psychology

Abstract

fetched live from OpenAlex

Abstract This paper examines two sociolinguistic lexical variables, 'work' and 'to dwell,' in the spoken French of former immersion students in their first or fourth year at a bilingual university in Ontario, Canada. Their patterns of use are compared to those of non-immersion graduates in the same institution, to Ontario high school immersion students, to former immersion students living in daily contact with French in Montreal, Canada, and to native speakers of Canadian French. The results suggest that, while under-performing in relation to the Montreal learners and the native speakers, the former immersion university students are at an advantage over their non-immersion university and their high school immersion counterparts in mastering socially stratified lexical variants, but that this advantage does not extend to socially neutral variants. The results are discussed in light of the relative levels of exposure to 'naturalistic' French experienced by the various speaker groups. Keywords: immersion educationcommunicative competencesecond languagesociolinguisticsFrenchCanada Notes 1. Members of Francophone communities in the English-language province of Ontario generally come into more sustained and intense contact with English than do members of such communities in the French-language province of Quebec.

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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.366
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.024
GPT teacher head0.396
Teacher spread0.372 · 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.

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

Citations12
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Bilingual Education and BilingualismSame topicLinguistic Variation and MorphologyFrench-language works237,207