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Record W2326114981

Discours et idéologies en immersion française

2015· article· fr· W2326114981 on OpenAlexaff
Sylvie Roy

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2015
Typearticle
Languagefr
FieldSocial Sciences
TopicLinguistic and Sociocultural Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsFrench immersionHumanitiesSociolinguisticsIdeologySociologyCompetence (human resources)FrenchPedagogyPsychologyLinguisticsPolitical scienceArtSocial psychologyPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

Abstract Résumé Cet article examine les idéologies linguistiques reliées à l’apprentissage du français langue seconde en immersion française. À partir d’extraits de discours d’élèves, d’enseignants, de parents et d’administrateurs, l’auteure examine les défis qui sont liés aux idées préconçues sur les langues auxquels les jeunes en immersion française font face. La sélection sociale des jeunes pour les différents programmes, la différence entre les programmes d’immersion précoce et tardive, les élèves allophones et la compétence des jeunes seront des thèmes traités. En prenant comme point de départ la sociolinguistique pour le changement, l’auteure pose des questions sur ces thèmes afin de faire réfléchir davantage sur ce qu’ils représentent pour les partis intéressés et comment des changements peuvent être apportés. Abstract This paper focuses on linguistic ideologies related to learning French as a second language in French immersion. Through the analysis of what students, teachers, parents and administrators say, the author looks at challenges faced by students in French immersion. Many of these challenges are related to preconceived ideas on languages. Themes involving the social selection of students in different programs, the differences between early and late French immersion, Allophone students in French immersion, and students’ competencies are explored. Adopting a sociolinguistics for change approach, the author also poses questions related to these themes. Asking questions allows stakeholders to reflect on the issues raised and on how change might be enacted in their own schools or classrooms.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0010.003
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.375
GPT teacher head0.596
Teacher spread0.221 · 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.

Study designNot applicable
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

Citations4
Published2015
Admission routes1
Has abstractyes

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