Identifying language profiles among multilingual children in kindergarten
Bibliographic record
Abstract
Distinguishing between a language difference due to dialect, learning additional language(s), and a developmental language disorder is important to provide equitable access to educational supports, yet is difficult in practice. Previous research has identified three main tasks (i.e. narrative, non-word repetition, and parent report) that are sensitive to developmental language disorder but less sensitive to language learning background. The current study applied an Integrated Knowledge Transfer approach and explored how these three measures could identify language learning profiles within a multilingual group of kindergarten-aged children in a French-language context. A total of 110 kindergarten-aged children participated in this study, including 22 children who were recruited based on being identified as having a developmental language disorder by a school or community speech-language therapist. While these two groups of children were not significantly different based on measures of the amount of language exposure, age of exposure to the language of school, or vocabulary in the language of school, a k-means cluster analysis identified three profiles based on narrative macrostructure, non-word repetition, and parent report. By understanding these language profiles, educators will be better positioned to provide equitable supports to linguistically diverse children and their families.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".