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Record W2995904629

Anglais intensif : transfert des savoirs et développement lexical

2019· article· fr· W2995904629 on OpenAlexaboutno aff
Nancy Gagné

Bibliographic record

VenueR-libre (Université Téluq) · 2019
Typearticle
Languagefr
FieldSocial Sciences
TopicFrench Language Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesEthnologyPolitical scienceSociologyArt
DOInot available

Abstract

fetched live from OpenAlex

« Anglais intensif : transfert des savoirs et développement lexical » par Nancy Gagné
\nRésumé :
\nCette étude se penche sur le développement lexical de jeunes francophones (n = 47, âge moyen : 11) inscrits dans un programme d’anglais intensif au Québec (Canada). La production orale des participants a été évaluée à partir d’une narration basée sur une série d’images (Derwing, Rossiter, Munro, & Thomson, 2004) afin de déterminer dans quelle mesure le programme de formation par compétences basé sur une approche communicative permet le développement lexical. Les résultats montrent que les élèvent améliorent leur diversité lexicale, qu’ils utilisent plus de mots anglais, mais que la densité et la sophistication n’augmentent pas de manière significative entre le début et la fin du programme.
\nMots clés : Acquisition d’une langue seconde - anglais intensif - contexte d’apprentissage formel - développement lexical
\n
\nAbstract : The study investigated the lexical development of French-speaking Grade 6 learners (n = 47 mean age : 11) enrolled in a 10-month intensive English program in Quebec, Canada. Measured at the beginning (Time 1) and after a 9-month study period (Time 2), oral production was assessed by means of a picture-cue narrative task (Derwing, Rossiter, Munro, & Thomson, 2004) to determine to what extent intensive English, based on a communicative approach in a competency-based program promotes lexical development. Findings revealed improvement between T1 and T2 in terms of number of English words, lexical diversity, but no significant improvement was found in terms of lexical sophistication and density.
\nKey words : Second language acquisition - intensive English - formal settings - lexical development

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.033
GPT teacher head0.292
Teacher spread0.259 · 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; both teacher heads agree on what is shown here.

Study designOther design
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

Citations0
Published2019
Admission routes1
Has abstractyes

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