Retelling a Script-Based Story: Do Children With and Without Language Impairments Focus on Script and Story Elements?
Bibliographic record
Abstract
PURPOSE: The script frameworks model (R. Schank, 1975) and causal network model (T. Trabasso & L. Sperry, 1985) were used to assess script-based story retellings of children with and without language impairments (LI). When retelling scripts and stories, children developing typically include (a) more obligatory than optional elements, with few temporal sequencing errors, and (b) story elements having several versus few causal connections to other story elements. The purpose of this study was to determine whether children with LI demonstrated a similar pattern of recall. METHOD: A script-based story retell was collected from 22 children with LI and 22 age-matched peers (AM). Retells were analyzed for inclusion of obligatory and optional elements, elements with high and low causal connectivity, and temporal sequencing accuracy. RESULTS: Retells from both groups contained more obligatory elements and elements with high causal connectivity. However, groups differed on the specific elements included. CONCLUSIONS: Children in the AM group appeared to utilize script and causal connectivity elements when retelling a script-based story. Children in the LI group appeared to focus more on script elements than causal connectivity. Their deficiencies may reflect difficulties with flexible application of scripts and accessing relevant knowledge, and/or generalized difficulties organizing information and extracting patterns.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".