Story Retell Narratives in Five School-Aged Children with Language Impairment
Bibliographic record
Abstract
Many children identified with Language Impairment (LI) demonstrate difficulty comprehending and producing narratives. Their narratives are often structurally less complex and of overall poorer quality than those produced by their typically developing peers. These difficulties may negatively impact the academic and social success of children with LI. This thesis evaluates the performance of five school-aged children with LI on a story retell probe embedded within an intervention designed to address their social and emotional language abilities. During the 10-week intervention, participants completed a series of story retell probes using wordless picture books. The story stimuli were taken from the Edmonton Narrative Norms Instrument, which included six stories (divided into two story sets), elicited twice (12 total story retells). The production of story grammar (SG) categories was analyzed for each story retell. The results for each participant and SG category varied greatly, but all participants had difficulty producing the more complex SG elements. Although each participant demonstrated some improvement from the first retell to the second on at least one story, overall performance remained fairly stable over the 10-week period. Future research is needed to determine effective ways to support more complex story narratives in children with LI.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.002 |
| Research integrity | 0.001 | 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".