Clarifying the developmental association between gesture and later vocabulary for autistic children
Bibliographic record
Abstract
Gestures serve as both a communication and a word-learning tool, with typically developing children consistently showing that early gestures are positively associated with later vocabulary skills. However, many autistic children experience delays and challenges in both gesture and vocabulary skills, and studies also show mixed gesture-vocabulary associations; thus, it is unclear whether gestures in autistic children support emerging vocabulary skills. To address previous conflicting findings, the current conceptual replication study uses linear models with a large sample (N = 451) of 12- to 24-month-old English-raised infants to investigate whether gestures are associated with expressive and receptive vocabulary. Using the infant-sibling design, gesture-vocabulary associations and group moderation were investigated in three groups: infant-siblings of autistic children who later meet the criteria for autism themselves (HL-ASD, n = 73), infant-siblings who did not meet criteria for autism (HL-Neg, n = 238), and a control group without a family history of autism (LL-Neg, n = 140). Both LL-Neg and HL-ASD groups showed positive associations between 12-month gestures and 18-month receptive vocabulary; however, only the LL-Neg group showed a positive association between 12-month gestures and 18-month expressive vocabulary. For 12-month gestures and 24-month receptive and expressive vocabulary, the LL-Neg and HL-Neg groups showed positive association, whereas the HL-ASD group did not. Similarly, the LL-Neg and HL-Neg groups showed positive associations between 18-month gestures and 24-month vocabulary, but the HL-ASD did not. Overall, the LL-Neg group showed significant gesture-vocabulary associations across all tested models, while the HL-ASD only showed one significant positive association.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".