Language-Specific Effects of Task Demands on the Manifestation of Specific Language Impairment: A Comparison of English and Icelandic
Bibliographic record
Abstract
PURPOSE: Previous research has indicated that the manifestation of specific language impairment (SLI) varies according to factors such as language, age, and task. This study examined the effect of task demands on language production in children with SLI cross-linguistically. METHOD: Icelandic- and English-speaking school-age children with SLI and normal language (NL) peers (n = 42) were administered measures of verbal working memory. Spontaneous language samples were collected in contexts that vary in task demands: conversation, narration, and expository discourse. The effect of the context-related task demands on the accuracy of grammatical inflections was examined. RESULTS: Children with SLI in both language groups scored significantly lower than their NL peers in verbal working memory. Nonword repetition scores correlated with morphological accuracy. In both languages, mean length of utterance (MLU) varied systematically across sampling contexts. Context exerted a significant effect on the accuracy of grammatical inflection in English only. Error rates were higher overall in English than in Icelandic, but whether the difference was significant depended on the sampling context. Errors in Icelandic involved verb and noun phrase inflection to a similar extent. CONCLUSIONS: The production of grammatical morphology appears to be more taxing for children with SLI who speak English than for those who speak Icelandic. Thus, whereas children with SLI in both language groups evidence deficits in language processing, cross-linguistic differences are seen in which linguistic structures are vulnerable when processing load is increased. Future research should carefully consider the effect of context on children's language performance.
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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 | 0.003 |
| 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 | 0.000 | 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".