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Second Language Learning and Cultural Representations: Beyond Competence and Identity

2006· article· en· W2099334334 on OpenAlexaff
Sara Rubenfeld, Richard Clément, Denise Lussier, Monique Lebrun, Réjean Auger

Bibliographic record

VenueLanguage Learning · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsUniversité du Québec à MontréalMcGill UniversityUniversity of Ottawa
Fundersnot available
KeywordsVitalityPsychologyLinguisticsCultural identityLanguage acquisitionSecond languageFirst languageCompetence (human resources)Social psychologyMathematics educationFeeling

Abstract

fetched live from OpenAlex

The socio‐contextual model of second language (L2) learning proposes that L2 learning is influenced by aspects of contact with the L2 community, L2 confidence, and identification to both the first language and L2 community ( Clément, 1980 ; Noels & Clément, 1996 ). The present study examines how these aspects are linked to individuals' cultural representations, corresponding to attitudes toward the L2 community ( Sperber, 1996 ). Respondents included Francophone (n= 50) and Anglophone (n= 50) university students with low and high ethnolinguistic vitality, respectively. Path analyses were conducted in order to examine the interrelations between aspects of the socio‐contextual model and cultural representations. These analyses revealed that, for both groups, learning an L2 leads individuals to hold more positive and accepting views of the L2 community. Implications of the findings are discussed with respect to ethnolinguistic vitality, L2 learning, and cultural representations.

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)0.005
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.008
Scholarly communication0.0070.005
Open science0.0000.004
Research integrity0.0010.001
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 head0.010
GPT teacher head0.277
Teacher spread0.267 · 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

Citations62
Published2006
Admission routes1
Has abstractyes

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