High‐frequency verbs and verb diversity in the spontaneous speech of school‐age children with specific language impairment
Bibliographic record
Abstract
Low verb diversity and heavy reliance on a small set of high-frequency 'general all purpose (GAP)' verbs have been reported to characterize specific language impairment (SLI) in preschool children. However, discrepancies exist about the severity of this deficit, particularly in whether these children's verb diversity is commensurate with their MLU level and whether verb diversity is more severely affected than general lexical diversity. Conflicting findings have been reported regarding the use of GAP verbs. This relatively large (n = 100) study extended the investigation of lexical diversity and high-frequency verb use to school-age children with SLI and NL peers and examined a particular hypothesis concerning the role of high-frequency verbs in language development. No differences were found between groups in general lexical diversity or verb diversity in samples of a set number of tokens. The results did not suggest that verb diversity constitutes an area of specific deficit in spontaneous production for children with SLI. SLI and NL groups were indistinguishable in high-frequency verb use. Extensive use of high-frequency verbs by both groups indicates that their use is part of normal development. Results are reported that support the hypothesis that high-frequency verbs act as prototypes for major meaning categories, permitting semantic and syntactic simplification with minimal losses in information value.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".