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Record W4409956610 · doi:10.1075/jicb.23031.sut

An exploration of oral language profiles of students in early French immersion

2025· article· en· W4409956610 on OpenAlexafffundabout
Ann Sutton, Elizabeth Kay‐Raining Bird, Fred Genesee, Xi Chen, Tamara Sorenson Duncan, Stephanie Pagan, Joan Oracheski

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsCarleton UniversityUniversity of TorontoMcGill UniversityDalhousie UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council
KeywordsImmersion (mathematics)LinguisticsPsychologyMathematics educationComputer scienceMathematicsPhilosophy

Abstract

fetched live from OpenAlex

Abstract This article describes an exploratory study of bilingual oral language profiles among a diverse group of students in Early French Immersion (EFI), in Ontario, Canada. Participants were 28 EFI students in Grade 4 (9–10 years of age). Oral language components were assessed with measures of comprehension (receptive vocabulary and following directions) and production (recalling sentences and mean length of utterance). Hierarchical cluster analysis was used to identify performance patterns in each language. There were substantial differences between English and French in terms of contrasts between, and consistency within the performance patterns, leading to identification of four bilingual profiles. The findings reinforce the importance of considering multiple language components in both languages in assessing bilingual students. A profiles approach may contribute to our understanding of variability in oral language skills and provide a broader perspective for the study of cross-linguistic interdependence by considering multiple components in both comprehension and production.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.158
Threshold uncertainty score0.313

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0020.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.037
GPT teacher head0.326
Teacher spread0.289 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Immersion and Content-Based Language EducationSame topicEFL/ESL Teaching and LearningFrench-language works237,207