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Record W4311681156 · doi:10.22215/etd/2022-15250

The Language Abilities of Children Considered At-Risk for Academic Difficulties Enrolled in Early French Immersion

2022· dissertation· en· W4311681156 on OpenAlexaff
Sarah Donnelly

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsCarleton University
Fundersnot available
KeywordsNeuroscience of multilingualismPsychologyTypically developingDual languageDevelopmental psychologyBilingual educationLinguisticsMathematics education

Abstract

fetched live from OpenAlex

Children with additional learning needs are disproportionately excluded from dual language education programs, in part because of concerns that bilingualism will exacerbate existing difficulties with language To address these concerns, this thesis investigates syntactic and morphosyntactic development in children with additional learning needs, who are registered in early French immersion (EFI). Participants were children who are often considered at-risk for academic difficulty (AR) enrolled in EFI (n = 13), children who were AR enrolled in an English-only program (ELoI; n =15), and children who were not AR enrolled in EFI (n = 10). No group differences were found between participant groups. The grammatical errors produced by children in each group were also examined and similar error patterns were observed across the three groups. These findings illustrate that children with additional learning needs are developing both English and French abilities when enrolled in EFI.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0040.000

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 head0.008
GPT teacher head0.290
Teacher spread0.282 · 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 designObservational
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
Published2022
Admission routes1
Has abstractyes

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