Contributions of Children's Linguistic and Working Memory Proficiencies to Their Judgments of Grammaticality
Bibliographic record
Abstract
PURPOSE: The authors explored the cognitive mechanisms involved in language processing by systematically examining the performance of children with deficits in the domains of working memory and language. METHOD: From a database of 370 school-age children who had completed a grammaticality judgment task, groups were identified with a co-occurring language and working memory impairment (LI-WMI; n = 18) or specific language impairment (SLI) with typical working memory skills ( n = 60) and matched control groups. Correct and incorrect use of grammatical markers occurred either early or late in sentence stimuli, imposing a greater working memory load for late-marker sentences. RESULTS: Children with SLI showed a lower preference for grammatical items than typically developing controls, regardless of error marker position. Children with LI-WMI demonstrated a performance pattern modulated by error marker position: Their preference for grammatical items was lower than typically developing controls for late but not early marker sentences. CONCLUSION: This pattern of results suggests that there are distinct and dissociable impacts of working memory and linguistic skills on metalinguistic functioning through a grammatical judgment task.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".