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Record W3097370193 · doi:10.37213/cjal.2020.30461

Evaluating the Oral Language Skills of English-Stream and French Immersion Students: Are the CLB/NCLC Applicable?

2020· article· en· W3097370193 on OpenAlexaffvenueabout
Diana Burchell, Kathleen Hipfner-Boucher, Janani Selvachandran, Patricia L. Cleave, Xi Chen

Bibliographic record

VenueCanadian Journal of Applied Linguistics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversité de MontréalDalhousie UniversityUniversité LavalUniversité du Québec à MontréalUniversity of Toronto
Fundersnot available
KeywordsFrench immersionNarrativeVocabularyActive listeningListening comprehensionPsychologyComprehensionLinguisticsLanguage proficiencyMathematics educationCommunication

Abstract

fetched live from OpenAlex

This study examined the oral language skills of grade-two anglophone children enrolled in French Immersion and English-stream programs. The study had two objectives: (a) to compare performance between the groups on measures of receptive vocabulary, narrative comprehension, and narrative production (i.e., structure and language) in English, and (b) to explore the applicability of the Canadian Language Benchmarks/Niveaux de compétences linguistiques canadiens (CLB/NCLC) to assessment of their conversational competency. All children (English-stream n = 27, French Immersion n = 33, aged 7-8 years) were tested in English. In addition, the French Immersion students were tested using equivalent measures in French. The results comparing performance in English revealed no differences between the groups on receptive vocabulary, narrative comprehension and narrative structure. However, the English-stream children outperformed their French Immersion peers in narrative language. Furthermore, CLB/NCLC listening and speaking criteria were applied to conversational samples yielding level scores in English (both groups) and French (French Immersion only). The range of benchmarks that are appropriate for this population is discussed in detail.

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)0.007
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 score0.611
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.054
GPT teacher head0.424
Teacher spread0.370 · 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

Citations2
Published2020
Admission routes3
Has abstractyes

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