Percent Grammatical Utterances Between 4 and 9 Years of Age for the Edmonton Narrative Norms Instrument: Reference Data and Psychometric Properties
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose The purpose of this article was to provide the reference data and evaluate psychometric properties for the percent grammatical utterances (PGU; Eisenberg & Guo, 2013 ) in children between 4 and 9 years of age from the database of the Edmonton Narrative Norms Instrument (ENNI; Schneider, Dubé, & Hayward, 2005 ). Method Participants were 377 children who were between 4 and 9 years of age, including 300 children with typical language (TL) and 77 children with language impairment (LI). Narrative samples were collected using the ENNI protocol (i.e., a story generation task). PGU was computed from the samples. Split-half reliability, concurrent criterion validity, and diagnostic accuracy for PGU were further evaluated. Results PGU increased significantly in children between 4 and 9 years of age in both the TL and LI groups. In addition, the correlation coefficients for the split-half reliability and concurrent criterion validity of PGU were all large ( r s ≥ .557, p s < .001). The diagnostic accuracy of PGU was also good or acceptable from ages 4 to 9 years. Conclusions With the attested psychometric properties, PGU computed from the ENNI could be used as an assessment tool for identifying children with LI between 4 and 9 years of age. The reference data of PGU could also be used for monitoring treatment progress. Supplemental Material https://doi.org/10.23641/asha.9630590
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it