Beyond Test Scores: Using Drawings and Language Samples to Characterize Multilingual Children's Language Profiles
Bibliographic record
Abstract
Abstract The United Nations Conventions on the Rights of the Child highlight the importance of children being involved in matters that concern them. Examining children's drawings can support speech-language pathologists' understanding of children's unique communication experiences, especially when considered alongside a language sample analysis (LSA). This study investigated drawings as a tool for use with multilingual children. The participants were 19 children aged 3 to 5 years who used Jamaican Creole and Jamaican English with either typical development (TD, n = 10) or developmental language disorder (DLD, n = 9). Children drew themselves talking, completed the Speech Activity and Participation Assessment of Children (SPAA-C), and provided language samples in both language contexts. Drawings were examined for themes and focal points, the SPAA-C was coded for emotion types, and language samples were analyzed using LSA measures (e.g., mean length of utterance, Index of Productive Syntax). The TD group represented themes more often within their drawings compared to the DLD group. Responses on the SPAA-C were generally positive for both groups. The TD group achieved higher scores across almost all LSA measures compared to the DLD group. The findings suggest that drawings, in concert with LSAs, may be a useful tool in understanding multilingual children's unique communication experiences.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".