Specific language or working memory impairments: A small scale observational study
Bibliographic record
Abstract
Study of the developmental relationship between language and working memory skills has only just begun, despite the prominent role of their interdependency in some theoretical accounts of developmental language impairments. Recently, Archibald and Joanisse (2009) identified children with specific language impairment (SLI), or specific working memory impairment (SWMI), or mixed language and working memory impairment (Mixed) based on standardized testing. In the present study, we report a first effort to provide clinical verification of these profiles by describing the social, behavioral, and academic characteristics of individual group members. Two each of children with SLI, SWMI, or Mixed impairments, individually paired with six typically developing classmates, were observed in their classroom, and their teachers completed questionnaires related to communication, working memory, and attention. Children with impairments were distinguished from typically developing children; however, relatively few patterns further distinguished the children with SLI, SWMI, and Mixed impairments. Interestingly, the children with memory impairments were found to have some language-related difficulties, and the children with language impairments, some memory-related difficulties. The limitations of these preliminary findings and future directions are discussed.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".