Value Added by Assessing Nonspoken Vocabulary in Minimally Speaking Autistic Children
Bibliographic record
Abstract
PURPOSE: There is a scarcity of language assessment tools properly adapted for use with minimally speaking autistic children. As these children often use nonspoken methods of communication (i.e., augmentative and alternative communication [AAC]), modification of traditional assessment tools is needed to capture the full range of their communicative repertoires. We modified the MacArthur-Bates Communicative Development Inventories (CDI) to explore how vocabulary size and composition are impacted by considering nonspoken, as well as spoken, expressive vocabulary (AAC-modified CDI: Words and Gestures). METHOD: Our initial sample consisted of 16 minimally speaking autistic children, 3-9 years old, whose caregivers completed our modified CDI after taking part in an AAC intervention. Our final sample included 15 participants, after removing an outlier. RESULTS: = .75). Verbs made up a sizable portion (13.3%) of vocabulary when accounting for all modalities, while nouns made up the majority (51.5%). CONCLUSIONS: We demonstrated the value of including both spoken and nonspoken modalities of communication when assessing the expressive vocabulary of minimally speaking autistic children. Prior work has shown that minimally speaking autistic children's spoken vocabulary was prominent in verbs (i.e., contained proportionally more verbs than that of vocabulary-matched typically developing children). In our sample, which used a broader definition of minimally speaking, we found that the proportions of verbs and nouns were consistent with what has been reported for typically developing children with similar-sized productive vocabularies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".