Who Does What to Whom: Introduction of Referents in Children’s Storytelling From Pictures
Bibliographic record
Abstract
PURPOSE: This article describes the development of a measure, called First Mentions (FM), that can be used to evaluate the referring expressions that children use to introduce characters and objects when telling a story. METHOD: Participants were 377 children ages 4 to 9 years (300 with typical development, 77 with language impairment) who told stories while viewing 6 picture sets. Their first mentions of 8 characters and 6 objects were scored as fully adequate, partially adequate, inadequate, or not mentioned. Total FM scores were compared across age and language groups. RESULTS: There were significant differences for age and language status, as well as a significant Age × Language interaction. Within each age group except age 9, children in the typical development group attained higher scores than children in the group with language impairment. CONCLUSION: These results suggest that the FM measure is a useful tool for identifying whether a child has a problem with introducing referents in stories.
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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 | 0.002 | 0.016 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".