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Record W3138961830 · doi:10.1075/jicb.20011.mar

Bilingual outcomes for a student with Down Syndrome in French immersion

2021· article· en· W3138961830 on OpenAlexaffabout
Sarah H. Martin, Christina Hodder, Emily Merritt, Ashley Culliton, Erin Pottie, Elizabeth Kay‐Raining Bird

Bibliographic record

VenueJournal of Immersion and Content-Based Language Education · 2021
Typearticle
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsDalhousie University
Fundersnot available
KeywordsFrench immersionNeuroscience of multilingualismAP French LanguagePsychologyReading (process)English languageBilingual educationPedagogyMathematics educationMedical educationLinguisticsLanguage assessmentMedicine

Abstract

fetched live from OpenAlex

Abstract This study investigated the French and English outcomes and experiences of one student with Down syndrome enrolled in a Canadian French Immersion (FI) program. Testing in Grades 6 and 8 revealed development in both languages, higher English than French skills, and progress across the two years in English only. English language and reading comparisons in Grade 8 showed the bilingual student had similar or better English abilities than age-matched monolinguals with Down syndrome (DS) schooled in English only. Interviews revealed that the parents were strong advocates for their son and worked closely with the school to ensure accommodations were in place in FI that fostered his success. The interviews also offered some explanation for the lack of French progress at second testing. This study provides the first evidence that FI can provide a path to bilingualism for students with DS. The findings have implications for inclusive education.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0050.001
Scholarly communication0.0020.000
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.323
Teacher spread0.304 · 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 designCase report
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

Citations1
Published2021
Admission routes2
Has abstractyes

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Same venueJournal of Immersion and Content-Based Language EducationSame topicLanguage Development and DisordersFrench-language works237,207