Evaluating the role of word-related parameters in the discriminative power of a novel nonword repetition task for bilingual children
Bibliographic record
Abstract
In bi- and monolingual children, nonword repetition tasks (NWRTs) differentiate typically developing (TD) children from children with Developmental Language Disorder (DLD) or children with a risk of DLD. Previous research has highlighted the importance of considering language specificity in nonword (NW) construction especially for bilingual children. A novel NWRT has been designed for the screening of DLD risk in the bilingual Italian-German preschool population, creating lists of language-specific (for the two target languages) and language-non-specific NWs. This study aimed to test the discriminative validity of this NWRT and to identify the characteristics of the NWs that maximise discriminative validity within language-specific and language-non-specific subsets. The findings confirm the role of language specificity (in terms of target language alikeness) but also of other characteristics related to word structure complexity.
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How this classification was reachedexpand
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".