The Use of Syntactic Cues in Lexical Acquisition by Children With SLI
Bibliographic record
Abstract
This study investigated the syntactic bootstrapping abilities of children who differed by language abilities and age. In the first study, the performance of 5-year-old children with Specific Language Impairment (SLI) was compared to that of two groups of typically developing children-one of equivalent language levels, as indexed by mean length of utterance (MLU), and the other of equivalent chronological age. In the second study, two groups of 7-year-old children, one whose language was developing typically and one with SLI, were involved. The count/mass distinction was used as the basis for the experimental tasks. A videotaped story was used to present the novel count and mass words, with syntactic cues in one condition and with neutral syntax in another. Results from the first study revealed that only the 5-year-old nonaffected control children showed evidence of using the syntactic cues. The 5-year-old SLI group and 3-year-old control group achieved comparable scores. However, error analyses suggested that different factors were operative in the two groups. The second study revealed that there was continued growth into the early school years for children with SLI and children whose language was developing typically.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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".